text:
\section{Introduction}

\label{sec:intro}

Recommendation services, event detection, clustering and categorization
of video data, and retrieval algorithms for large video databases depend on efficient and reliable similarity identification amongst video segments  \cite{arandjelovic2013all, chou2013near,wang2010large}. In a nutshell, given a query video, we wish to find all similar video segments within a large video database in the most reliable and efficient way. The state-of-the-art in similarity identification hinges on video fingerprinting algorithms \cite{revaud2013event, douze2013stable}. The aim of such algorithms is to provide for distinguishable representations that remain robust under visual distortions, such as, rotation, compression, blur, resizing, flicker, etc. Such distortions are expected to be present within large video collections, or when dealing with content {\textquotedblleft \textit{in the wild}\textquotedblright} \cite{wang2010large}.

In a broad sense, video similarity identification can be seen as a spatio-temporal matching problem via an appropriate feature space or descriptor. Recent results  have shown that similarity identification algorithms based on local descriptors, such as the scale invariant feature transform (SIFT) \cite{lowe1999object} or dense SIFT \cite{vedaldi2010vlfeat}, tend to significantly outperform previous approaches based on histogram methods \cite{hampapur2001comparison} or fingerprinting algorithms \cite{esmaeili2011robust}, especially under the presence of distortions in the video data. Therefore, the state-of-the-art in this area is based on vectors of locally aggregated descriptors (VLAD) \cite{jegou2012aggregating}, or Bag-of-Words (BoW) methods \cite{yang2007evaluating}, which merge feature descriptors in video frame regions. More recently, hyper-pooling approaches have been proposed \cite{douze2013stable}, which perform two consecutive VLAD stages in order to compact entire video sequences into a unique aggregated descriptor vector.

In this paper, we focus on VLAD-based algorithms and examine the problem
of creating compact representations that are suitable for efficient
and accurate similarity identification of segments of videos within
a large video collection. The paper makes the following contributions:
\begin{itemize}
\item Instead of creating holistic hyper-pooling approaches for entire
video sequences, we concentrate on groups of frames (GoFs) within
a video sequence in order to allow for video segment search.
\item Instead of directly compacting  feature descriptors, we follow a two-stage clustering approach: we first cluster features to obtain local-feature-centers (LFCs)  and then encode the latter with respect to a given set of
centers of local-feature-centers (CLFCs),  computed from a training set.
\item Similar to  VLAD,
 we encode the LFCs by aggregating their differences with respect to their
corresponding CLFCs, thereby creating \emph{vectors of locally aggregated
centers} (VLAC).
\item Experiments using a 100-minute  training  set and a 1000-minute
test set from the Open Video Project reveal that, for the same compaction
factor, our proposal is outperforming the state-of-the-art VLAD method \cite{jegou2012aggregating} by more than  15\% in terms of mean Average Precision (mAP).
\end{itemize}
The remainder of the paper is  as follows. Section \ref{sec:Background}
summarizes the operation of VLAD and hyper-pooling  that constitute the state-of-the-art and form the basis of the proposed compaction algorithm.
Section \ref{sec:VLAC} presents the proposed VLAC approach. Section
\ref{sec:Evaluation} presents experimental results, while  Section \ref{sec:Conclusion}
draws concluding remarks.

\section{Background on VLAD and Hyper-Pooling}
\label{sec:Background}

\subsection{Visual Feature Description}

Current solutions make use of image descriptors to represent individual
frames within a video \cite{revaud2013event, douze2013stable}. After extracting the local feature descriptors of a given set of frames using an algorithm such as SIFT \cite{lowe1999object} or dense SIFT �\cite{vedaldi2010vlfeat}, these descriptors are then accumulated
to produce a compact frame representation. Recent work advocated the
use of pooling strategies instead of simple averaging methods, in
order to minimize information loss. A common way to achieve this is
by using BoW methods \cite{yang2007evaluating} or VLAD \cite{jegou2012aggregating}. In this paper, we focus on the latter
as it has been shown to achieve state-of-the-art results in terms
of mAP in medium and large-scale sets of image and video content.

\subsection{Vector of Locally Aggregated Descriptors}

VLAD \cite{arandjelovic2013all, jegou2012aggregating} is a vector aggregation algorithm that produces a fixed-size
compact description of a set comprising a variable number of data
points. VLAD was proposed as a novel approach aimed to optimize:
\textit{(i)} the representation of aggregated feature descriptors;
\textit{(ii)} the dimensionality reduction;
\textit{(iii)} the indexing of the output vectors.

These aspects are interrelated---for example, dimensionality reduction
directly affects the way we index the output vectors. While high dimensional
vectors produce more accurate search results, low dimensional
vectors are easier to index and require less operations and storage.

Consider a set of $W$ video frames to be used for training purposes.
For the $w$th training frame, $1\leq w\leq W$, a visual feature
detector and descriptor (e.g., the SIFT detector and descriptor \cite{lowe1999object})
is calculated, thereby producing $K_{w}$
feature vectors $\mathbf{f}_{w,k}$, $1\leq k\leq K_{w}$, each with dimension $1\times F$. The ensemble
of these features comprises the $w$th training frame's set of visual
features $\mathfrak{\mathcal{F}}_{w}=\left\{ \mathbf{f}_{w,1},\,\mathbf{f}_{w,2},...,\,\mathbf{f}_{w,K_{w}}\right\} $.
The concatenation of all these sets for all $W$ training frames,
given by $\mathcal{F}_{\text{train}}=\left\{ \mathcal{F}_{1},\,\mathcal{F}_{2},...,\,\mathcal{F}{}_{W}\right\} $,
undergoes a clustering approach, such as K-means \cite{bishop2006pattern}, thereby
grouping all vectors in $\mathfrak{\mathcal{F}}_{\text{train}}$ into
$J$ clusters, with centers denoted by set $\mathcal{C}_{\text{train}}=\{\mathbf{c}_{1},\,\mathbf{c}_{2},...,\,\mathbf{c}_{J}\}$.
VLAD then encodes the set of visual features, $\mathfrak{\mathcal{F}}_{w}$,
of the $w$th frame as the group of $F$-dimensional vectors $\mathbf{v}_{w,j}$
($1\leq j\leq J$) given by
\begin{equation}
\mathbf{v}_{w,j}=\sum_{\forall k:\: Q\left(\mathbf{f}_{w,k}\right)=\mathbf{c}_{j}}\left(\mathbf{f}_{w,k}-\mathbf{c}_{j}\right)\label{eq:VLAD_center_coding}
\end{equation}
where $Q\left(\mathbf{f}_{w,k}\right)$ is the quantization function
that determines which cluster $\mathbf{f}_{w,k}$ belongs to.
Then, the VLAD of the $w$th frame is given by the
vector of aggregated local differences
$\mathbf{v}_{w}=\begin{bmatrix}\mathbf{v}_{w,1} & \cdots & \mathbf{v}_{w,J}\end{bmatrix}$,  with dimension $1\times JF$.
All these vectors are concatenated into the $W\times JF$-dimensional
matrix $\mathbf{V}_{\text{train}}=\begin{bmatrix}\mathbf{v}_{1} & \cdots & \mathbf{v}_{W}\end{bmatrix}^{\text{T}}$,
which comprises the VLAD encoding of the training set. In order to
allow for further dimensionality reduction (thereby accelerating the
matching process), principal component analysis (PCA) is applied to $\mathbf{V_{\text{train}}}$,
and the $D$ most dominant eigenvectors are maintained in the $D\times JF$
matrix $\mathbf{P}_{\text{train}}$ in order to be used in the test
set.

When considering a test video frame, once its set of visual features
$\mathfrak{\mathcal{F}}_{\text{test}}=\left\{ \mathbf{f}_{\text{test},1},\,\mathbf{f}_{\text{test},2},...,\,\mathbf{f}_{\text{test},K_{\text{test}}}\right\}$
is produced by the SIFT descriptor (assuming $K_{\text{test}}$ points
were detected), VLAD performs the following step: \emph{(i)} calculation
of $\mathbf{v}_{\text{test},j}$ ($1\leq j\leq J$) via \eqref{eq:VLAD_center_coding}
with the precalculated center set $\mathcal{C}$; \emph{(ii)} aggregation
of these into a $JF\times1$ composite vector and application of dimensionality
reduction via the retained PCA coefficients in $\mathbf{P}_{\text{train}}$:
\begin{equation}
\mathbf{v}_{\text{test}}=\mathbf{P}_{\text{train}}\begin{bmatrix}\mathbf{v}_{\text{test},1} & \cdots & \mathbf{v}_{\text{test},J}\end{bmatrix},\label{eq:VLAD_coding}
\end{equation}
where $\mathbf{v}_{\text{test}}$ denotes the $D\times1$ VLAD of
the test video frame after compaction with PCA. The similarity between
two VLAD vectors of two test video frames $t_1$ and $t_2$
is simply measured via $s_{t_1,t_2}=\left\langle \mathbf{v}_{t_1},\mathbf{v}_{t_2}\right\rangle $.
Thresholding the set of similarity (i.e., inner product)\ results between a test video frame
and the entire test set of video frames provides the list of similar
frames retrieved under the selected threshold value.

\subsection{Hyper-Pooling}

A recent method proposed by Douze \emph{et al.}~\cite{douze2013stable} makes use of hyper-pooling (HP) strategies on the video description level.
Hyper-pooling works by using a second layer of data clustering and
encoding a set of frame VLAD descriptors into a single vector.
Hyper-pooling utilizes an enhanced hashing scheme by exploiting the
temporal variance properties of  VLAD vectors \cite{douze2013stable} that have been produced per frame. After
performing  PCA, the temporal variance of VLAD vectors is
most prominent in the components associated with  low eigenvalues. Hence,  hyper-pooling postulates that we can get a more stable set of centers
by applying a clustering algorithm (such as K-means) on the set of
components relating to the highest eigenvalues. Indeed, hashing the
components that vary less with time has been shown to provide better
results in terms of stability and robustness to noise \cite{douze2013stable}.

\subsection{Motivation Behind the Proposed Concept\label{sec:Motivation}}

From the previous description, it is evident that the crucial aspects
of VLAD and hyper-pooling are the clustering and the PCA process performed
on the training set. Ideally, for a given set of video frames, we
would like to produce principal component vectors  for compaction of VLADs that do
not change substantially when the video frames undergo real-world visual distortions. For
example, consider two ensembles of training video frame sets, $\mathcal{I}_{\text{clean}}$
and $\mathcal{I}_{\text{noisy}}$, with the latter  produced by
distorting the video frames in  $\mathcal{I}_{\text{clean}}$
via blurring, compression artifacts, rotation, gamma changes, etc.
During the training stage, applying PCA on the  vectors of local differences (obtained per frame) will produce $D$ dominant eigenvectors forming the  $D\times JF$ matrices $\mathbf{P}_{\text{train,clean}}$
and $\mathbf{P}_{\text{train,noisy}}$. In case of  hyper-pooling the aforementioned matrices will have a dimension of $D\times JD_{0}$, where  $D_{0}$ is  the number of dimensions retained  after the first VLAD stage. Ideally,
the vectors in $\mathbf{P}_{\text{train,clean}}$ and $\mathbf{P}_{\text{train,noisy}}$
should be reasonably-well aligned, which is an indication that the
 compaction process is robust to noise. This can be tested by
computing the sum-of-inner-products between the $D$ dominant eigenvectors
of both cases: For both VLAD and hyper-pooling, we obtain
\begin{align}
s_{\text{\{VLAD,HP\},clean,noisy}}&= \nonumber\\
&\sum_{i=1}^{D}\sum_{j=1}^{\left\{ JF,JD_{0}\right\} }p_{\text{train,clean}}\left[i,j\right]p_{\text{train,noisy}}\left[i,j\right]
\end{align}
where $p\left[i,j\right]$ denotes the $\left(i,j\right)$ element
of $\mathbf{P}$.
We carried out such an indicative test in a set of $Z=2000$ video
frames taken from 10 video clips of 10-minute duration each. Each video underwent seven different
visual distortions, as tabulated in  Table \ref{tab:Distortions} and detailed in  Section \ref{sec:Evaluation}.
Using $J=128$ clusters for VLAD and
$F=128$ for dense SIFT,  we obtain $s_{\text{VLAD,clean,noisy}}=0.0085$
and $s_{\text{HP,clean,noisy}}=-0.0445$.  However,  utilizing the SIFT vectors directly, performing PCA decomposition to produce the two $D\times F$
matrices $\mathbf{P}_{\text{SIFT,train,clean}}$ and $\mathbf{P}_{\text{SIFT,train,noisy}}$,
and computing
\begin{equation}
s_{\text{SIFT,clean,noisy}}=\sum_{i=1}^{D}\sum_{j=1}^{F}p_{\text{SIFT,train,clean}}\left[i,j\right]p_{\text{SIFT,train,noisy}}\left[i,j\right]
\end{equation}
we get $s_{\text{SIFT,clean,noisy}}=0.996$. The significant difference
between $s_{\text{SIFT,clean,noisy}}$ and $s_{\text{VLAD,clean,noisy}}$
and $s_{\text{HP,clean,noisy}}$ represents the reduction in tolerance
to distortions incurred when the vectors are projected to their principal components, which is performed in order  to gain the benefit of compaction.

In this paper, our aim is to design a method leading to
the same compaction factor as VLAD, albeit having increased tolerance
to distortion in the video frames, which will allow for high recall
rates even when dealing with distorted versions of the input video
content. A secondary aim is to design our approach in a way that directly
deals  with video segments rather that individual video frames, thus allowing for video segment similarity detection. These two
aspects are elaborated in the next section.

\section{Vector of Locally Aggregated Centers\label{sec:VLAC}}
\label{sec:VLAC}

\subsection{VLAD per Video Frame}

The similarity between two videos can be estimated by obtaining the VLAD inner products per frame and averaging. We consider this approach as the baseline for video similarity
detection. This direct application of VLAD to video achieves good results
in terms of retrieval accuracy, albeit at the expense of high complexity
and storage requirements, even when the video is sampled at a substantially
lower frame-rate. All the solutions proposed are designed to approach
the performance of this baseline\  as much as possible while
requiring a fraction of its computational complexity and storage, or, alternatively, significantly-exceed the VLAD\ performance while incurring the same complexity and storage.

\subsection{Temporal Compaction for Video Segment Searching}

Video description algorithms such as hyper-pooling \cite{douze2013stable}  were designed for holistic
video description, namely,  the derived  vector  describes the entire video information \emph{as
a whole}. Temporal coherency is lost when using such holistic description
methods, thereby making the detection of video segments within longer
videos impossible. This problem can be solved by modifying holistic
solutions to work on \emph{groups of frames} (GoFs) within each given
video.  GoFs can be
viewed as fixed-size temporal windows, each of which is then compacted
into a single VLAD, hyper-pooling or VLAC descriptor (referred to as VLAD-GoF, HP-GoF and VLAC-GoF, respectively). A video segment
can then be matched by finding maximum  inner product between its VLAD-GoF,
HP-GoF, or VLAC-GoF descriptor and the corresponding
descriptor from the a GoF in the video. Evidently, the length
of the GoF controls the accuracy of the detection of video segments
within longer videos. In addition, GoFs can also be  overlapping
to allow for better temporal resolution within the matching process.

\subsection{Proposed Vector of Locally Aggregated Centers}

Instead of clustering the local descriptors found within each GoF,
we propose to  cluster the centers of clusters of local descriptors.
The aim is to produce results that are increasingly robust to distortions
that may be found in a typical large video database. Encoding centers
is expected to be more robust to such visual distortions since, compared to local feature descriptors, the centers
of local feature descriptors will vary less when artifacts from processing
 are incurred on video frames.

Consider $T$ training GoFs stemming from a set of training videos.
From the frames of each $\tau$th GoF ($\tau\in[1, \dots,T]$),
we extract a set of $K_{\tau}$ dense SIFT feature
vectors $\mathfrak{\mathcal{F}}_{\tau}=\left\{ \mathbf{f}_{\tau,1},\,\dots,\mathbf{f}_{\tau,K_{\tau}}\right\}$, each having $F$ dimensions.
From each  $\mathcal{F}_{\tau}$, we calculate  $N$ local feature centers (LFCs)
 $\mathcal{C}_{\tau}=\left\{ \mathbf{c}_{\tau,1},\,\dots,\mathbf{c}_{\tau,N}\right\}$. By concatenating the LFCs for each $\tau$, we acquire the training set of LFCs  $\mathfrak{\mathcal{C}_{\text{train}}}=\left\{ \mathfrak{\mathcal{C}_{1}},\,\dots,\mathfrak{\mathcal{C}_{\mathrm{\mathcal{\mathit{T}}}}}\right\} $.
We then apply a second stage of clustering on  $\mathcal{C}_{\text{train}}$ to generate a set
of $M$   centers of LFCs (CLFCs)  $\mathcal{C}_{\text{enc}}=\left\{ \mathbf{c}_{\text{enc},1},\,...,\,\mathbf{c}_{\text{enc},M}\right\}$,  where each CLFC has $F$ dimensions.

We now consider a test video query $u_{i}$ that contains $G$ GoFs. For
every $g\in[1,\dots, G]$, we extract $K_{g}$ local features
to obtain $\mathfrak{\mathcal{F}}_{g}=\left\{ \mathbf{f}_{g,1},\,...,\,\mathbf{f}_{g,K_{g}}\right\} $.
Then, for every $\mathfrak{\mathcal{F}}_{g}$, we obtain a set of $N$ local feature centers $\mathcal{C}_{g}=\left\{ \mathbf{c}_{g,1},\,...,\,\mathbf{c}_{g,N}\right\}$. Using VLAD we encode each
set of centers $\mathcal{C}_{g}$ with the set of trained centers
$\mathcal{C}_{\text{enc}}$ to generate a vector of locally aggregated
centers (VLAC). Particularly, we first obtain the $F$-dimensional
vector $\mathbf{v}_{g,m}$ for each center $\mathbf{c}_{\text{enc},m}$
in $\mathcal{C}_{\text{enc}}$ by applying
\begin{equation}
\mathbf{v}_{g,m}=\sum_{\forall n:\: Q\left(\mathbf{c}_{g,n}\right)=\mathbf{c}_{\text{enc},m}}\left(\mathbf{c}_{g,n}-\mathbf{c}_{\text{enc},m}\right).\label{eq:VLAD_center_coding-1-1}
\end{equation}
The VLAC for $g$ is then obtained by concatenating $\mathbf{v}_{g,m}$
for all $m\in[1, 2,, \dots,M]$ into a single $1\times MF$-dimensional vector $\mathbf{v}_{g}=\begin{bmatrix}\mathbf{v}_{g,1}, \dots, \mathbf{v}_{g,M}\end{bmatrix}$. We observe that $N$ does not affect the dimension of VLAC, but serves as a control variable for the \emph{coarseness} of the description. After calculating $\mathbf{v}_{g}$ for all $g\in[1,2,,\dots,G]$, we project them on a trained set of $D$ principal eigenvectors to perform  dimensionality reduction. We then concatenate these vectors  to generate
a compact $G\times D$-dimensional vector $\mathbf{v}_{u_{i}}=\begin{bmatrix}\mathbf{v}_{u_{i},1} & \cdots & \mathbf{v}_{u_{i},G}\end{bmatrix}$
for video $u_{i}$. The similarity between two videos $u_1$ and $u_2$  is given
by calculating $s_{u_1,u_2}=\left\langle \mathbf{v}_{u_1},\mathbf{v}_{u_2}\right\rangle $. A threshold is then applied on $s_{u_1,u_2}$ to determine whether
the videos are similar. If two videos contain a different number of
GoFs (e.g.,    $G_{1}$ and $G_2$ GoFs with  $G_{2}>G_{1}$), $s_{u_1,u_2}$ is calculated for all possible alignments $k$
of the vectors $\mathbf{v}_{u_1}$ and $\mathbf{v}_{u_2}$. Finally, the maximum over $k$
is taken to be the similarity score. This can be expressed as
\begin{align}
s_{u_1,u_2} = \max\sum_{g=1}^{G_{1}}&\left\langle \mathbf{v}_{u_1,g},\mathbf{v}_{u_2,g+k}\right\rangle\nonumber\\ &\forall k\,\in\{1,\,2,\,...,(G_2-G_1)\}
\end{align}

Examining the performance of VLAC\ under the experiment of Section \ref{sec:Motivation}, we obtain  $s_{\text{VLAC,clean,noisy}}=0.1131$,  which is more than 13 times higher than   $s_{\text{\textbraceleft VLAD,HP\textbraceright,clean,noisy}}$. We therefore expect the proposed method to be significantly more robust than VLAD and hyper-pooling when assessing video similarity under noisy conditions. However, in order to be suitable for video retrieval, it must also be \textit{discriminative}, i.e., be able to differentiate between \textit{dissimilar} videos that would inherently lead to different features. This is assessed experimentally in the following section.   \

\section{Evaluation of Video Descriptors\label{sec:Evaluation}}
\label{sec:Evaluation}

\subsection{Dataset}

We selected 100 random videos from the Open Video Project (OVP), comprising 1000 minutes of video. Seven types of distortions (Table \ref{tab:Distortions}) were applied
to this footage to examine the performance of VLAD, hyper-pooling (HP)
and VLAC under noise. Training for VLAD, VLAC and HP\ centers was done on different OVP videos from the utilized test material.

To generate the queries, one-minute video segments were extracted  from each  original videos.
Then, the dataset and query videos were sampled at a  rate of
$\frac{1}{3}$ frames-per-second (fps). The sampling of the query videos, however, is shifted by 0.25 seconds
with respect to the sampling of the videos in the dataset. In this way,  sampling misalignments were also taken into account.  First, we evaluate the similarity detection of the proposed VLAC versus the state-of-the-art VLAD when both are extracted from each sampled frame in the sequence (that is, $\text{GoF}=1$). For VLAD, we set $J=128$, while for VLAC we use $N=128$ and $M=16$. This provides an upper bound on the detection accuracy and assesses the performance of the proposed method versus the standard per-frame VLAD. Next,  the proposed VLAC-GoF is compared against VLAD-GoF\ and HP-GoF, where one descriptor per GoF of 5 frames is derived and the overlap is set to  one frame. Concerning the parameters for each method, we use $J=128$ for VLAD-GoF, $N=256$ and $M=16$ for VLAC-GoF. For HP-GoF,  the number of centers used to encode the first stage VLAD is $\alpha_1 = 128$ and for the second stage $\alpha_2=32$, where we keep $512$ dimensions from the first stage VLAD.

\begin{table*}[t]
\centering
\begin{tabular}{|c|c|}
\hline
Distortion & Parameters\tabularnewline
\hline
\hline
Scaling & \textbf{FFMPEG:}\texttt{-vf scale=iw/2:-1}\tabularnewline
\hline
Rotation & \textbf{FFMPEG:}\texttt{-vf rotate} $\frac{\pi}{15}$\tabularnewline
\hline
Blurring & \textbf{FFMPEG:} \texttt{-vf boxblur 1:2:2}\tabularnewline
\hline
Compression  & \textbf{FFMPEG:} \texttt{-crf 35}\tabularnewline
\hline
Gamma Correction & \textbf{FFMPEG:} \texttt{-vf mp 1:1.2:0.5:1.25:1:1:1}\tabularnewline
\hline
Flicker & \textbf{OpenCV:} Random brightness change (120\%--170\%)\tabularnewline
\hline
Perspective Change & \textbf{OpenCV} AffineTransform triangle  \texttt{[(0,0),(0.85,0.1),(0,1)]}\tabularnewline
\hline
\end{tabular}
\caption{Set of  distortions applied to the videos in the database.}
\label{tab:Distortions}
\end{table*}

\begin{figure}
\subfigure[]{
\includegraphics[width=.52\textwidth,trim=.2cm 0.2cm 0.2cm 0.3cm]{fig128}}
\subfigure[]{
\includegraphics[width=.52\textwidth,trim=.2cm 0.2cm 0.2cm 0.3cm]{fig256}}
\caption{Precision versus recall for VLAD \cite{jegou2012aggregating} and the proposed VLAC,
when extracted per each frame (GoF $=1$); (a) $D=128$ and (b) $D=256$.}
\label{fig:figPerFrame}
\end{figure}

\subsection{Performance and Results}
Fig. \ref{fig:figPerFrame} depicts the precision versus the recall achieved with the proposed VLAC and the state-of-the-art  VLAD \cite{jegou2012aggregating}, when both descriptors are extracted from each of the frames in the compared video segments.  The results  show that the proposed descriptor offers a substantial detection accuracy improvement compared to VLAD across the entire precision-recall range.  The improved performance of VLAC can be explained by its improved tolerance  to noise, i.e., $s_{\text{VLAC,clean,noisy}}>s_{\text{\textbraceleft VLAD,HP\textbraceright,clean,noisy}}$, which indicates that the principal component projections do not vary substantially after the application of distortions. Therefore, VLAC  retains more information after being projected on its trained principle components.
Note that the
training videos used to generate the principal
components   did not have any distortions applied on them; this is
to simulate real-life
conditions where we cannot predict the distortions in the dataset.
In addition, all distortions were applied on all videos in the dataset, meaning that higher recall
reflects higher tolerance to distortions. Same observations can be made from the results in Fig. \ref{fig:figGoF}, where our VLAC is compared against VLAD and hyper-pooling for a GoF of size 5 frames.

\begin{figure}
\subfigure[]{
\includegraphics[width=.52\textwidth,trim=.2cm 0.2cm 0.2cm 0.3cm]{fig128GoF}}
\subfigure[]{
\includegraphics[width=.52\textwidth,trim=.2cm 0.2cm 0.2cm 0.3cm]{fig256GoF}}
\caption{Precision versus recall for VLAD \cite{jegou2012aggregating}, HP \cite{douze2013stable}, and the proposed VLAC under $\text{GoF} = 5$ and the overlap is 1 frame; (a) $D=128$ and (b) $D=256$.}
\label{fig:figGoF}
\end{figure}

Table \ref{tab:mAP}
shows the mean average precision (mAP) for the three compared methods, where $D$ is
the number of dimensions after projection. The results show that, under the same $D$, VLAC improves the mAP by $28.65\%- 38.00\%$ compared to VLAD for frame-by-frame matching and $23.39\%- 26.56\%$ for GoF-based matching. The improvement offered by VLAC-GoF over HP-GoF reaches up to $63.10\%$.

\begin{table}[t]
\centerin
\begin{tabular}{|c|c|c|}
\hline
 & $D$ & mAP\tabularnewline
\hline
\hline
VLAD \cite{jegou2012aggregating}  & 256 & 0.7462\tabularnewline
\hline
 & 128  & 0.6761\tabularnewline
\hline
Proposed VLAC & 256 & 0.9600\tabularnewline
\hline
 &  128 & 0.9330\tabularnewline
\hline
VLAD-GoF \cite{jegou2012aggregating}  & 256 & 0.5647\tabularnewline
\hline
 & 128  & 0.5262\tabularnewline
\hline
Proposed VLAC-GoF & 256 & 0.7147\tabularnewline
\hline
 &  128 & 0.6493\tabularnewline
\hline
HP-GoF \cite{douze2013stable} & 256 & 0.4382\tabularnewline
\hline
 & 128  & 0.4135\tabularnewline
\hline
\end{tabular}
\caption{Mean Average Precision (mAP) for VLAD \cite{jegou2012aggregating}, HP \cite{douze2013stable} and the proposed
VLAC under: frame-by-frame operation (top two) and GoF-based operation (bottom three).}
\label{tab:mAP}
\end{table}

\section{Conclusion\label{sec:Conclusion}}
\label{sec:Conclusion}
We proposed a novel compact video representation
method based on aggregating local feature centers. Our results show that encoding local feature centers yields significantly better results than simply encoding the features, which are less tolerant to visual distortions commonly found in video databases.  The proposed approach  is therefore suitable for video similarity detection with robustness to visual distortions. The recall-precision results were improved without incurring extra complexity in the signature matching process. Future work will assess the performance of the proposed approach under uncontrolled distortion conditions and even larger datasets.

\bibliographystyle{IEEEbib}
\nocite{*}

redpajama_set_name: RedPajamaArXiv


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If you suffer from symptoms such as: Headaches, flushes, blocked nasal cavities and IBS after a glass of wine, then you may have an allergy to sulphites. Most of us enjoy a glass of wine or two from time to time, but for those of us with a predisposition towards wine allergies and the resulting after effects, it can ruin the experience. UBfree wine drops is a New Zealand made, organic product developed to combat these effects. We caught up with the couple behind the cure, Christina and Miles Clifford, to find out more.
So first of all, how did you find out about these wine drops and how have they helped you and people you know to deal with wine allergies and headaches?
It all started when I was unable to drink wine without having headaches and nasal congestion. I wondered if it was the sulphites in wine so I questioned my GP about it. She confirmed my thinking. As I thoroughly enjoy drinking wine I really didn't want to choose another drink so both Miles and I went on a search for a solution. We started asking lots of questions about sulphites and why are they in wine which led to the development of the wine drops. We had an initial formula and engaged the services of a food scientist who helped us refine the formula. It worked amazingly for me and for friends who were also suffering. After 18 months of research and development we took it to market in 2013. Since then many people are now able to enjoy wine again. We have a customer who couldn't drink wine for 25 years and decided to try our product. She was absolutely thrilled as she now enjoys a glass of wine without any allergy-like symptoms.
Could you explain, in layman terms, how these drops will neutralise the compounds associated with allergies to wine?
UBfree Wine Drops works in two ways. Firstly it oxidises the wine which reduces the free sulphites and secondly the mineral we add allows your body to naturally digest the bound sulphites in wine. In effect we are making the sulphites neutralised so your body can naturally do the rest.
Is this product aimed solely at people who suffer from sulphite allergies, or is there a benefit for others?
UBfree Wine Drops can be viewed as a preventative measure. As our bodies age, we can become more susceptible to sulphite intolerance. People who use our product, who aren't sulphite intolerant, report that they feel better when consuming wine and it tastes smoother.
Is it true that UB drops can actually improve the quality of some young wines by 'cellaring' them?
And finally, where is your business headed?
We have been so excited and thrilled with the response in the market place so far. UBfree Wine Drops sells themselves and being a New Zealand made, organic product is definitely beneficial. We are working hard towards making this product available in all supermarkets, health shops, pharmacies and liquor outlets. In five years we would love UBfree Wine Drops to be a household name.


text:
Caroline J. Berdzik Named to The National Law Journal's Health Care Trailblazers
Caroline J. Berdzik
Caroline J. Berdzik, a partner based in Goldberg Segalla's Princeton office and a member of the firm's most senior leadership, has been named to The National Law Journal's 2021 list of Health Care/Life Sciences Trailblazers, which recognized her as an "agent of change."
Caroline was recognized for leveraging her leadership experience as a former in-house counsel to one of New Jersey's largest privately held long-term health care companies to lead Goldberg Segalla's national Health Care, Employment and Labor, and Long-Term Care practices. She has blazed numerous trails throughout her career, including chairing the Defense Research Institute's national conference on COVID-19 liability for senior care and long-term care providers. This led to her being honored with the organization's Albert H. Parnell Outstanding Program Chair Award for innovative programming for in-house counsel and insurance companies. She also continues to develop COVID-19-related protocols, which has become a turning point for many health care providers.
"There was no blueprint," Caroline said in an interview with the National Law Journal. "And there are a lot of moving pieces, with the Centers for Medicare and Medicaid Services not providing much guidance. And we are currently putting together similar protocols for vaccination."
Caroline also shared that she believes the COVID-19 pandemic will lead to continued facility closings, consolidations, and challenges for providers to secure insurance.
"We will see if the government will step up and provide more funding so that wages can increase for caregivers," Caroline said. "It's a highly regulated industry—more so than nuclear energy. And there are questions whether providers will be able to continue to deliver care and be viable going forward."
MORE ABOUT GOLDBERG SEGALLA'S CAROLINE J. BERDZIK:
Caroline J. Berdzik, chair of the firm's Employment and Labor, Health Care, and Long-Term Care groups, devotes her practice to helping businesses, organizations, and management navigate the panoply of employment law issues, from proactive counseling through alternative dispute resolution and trial. In June 2020 the firm's equity partnership elected Caroline to serve on the firm's five-person Facilitation Committee overseeing 400 attorneys across 21 offices in 10 states. She is also co-chair of the firm's Women's Initiative. She has earned numerous honors that recognize the value of her legal services and her dedication to the community, including her recognition as one of the "Best 50 Women in Business" by NJBIZ and her regular appearance among the rankings of top legal practitioners in New Jersey and the New York Metro area.
Health Care Long-Term Care Employment and Labor Caroline J. Berdzik

redpajama_set_name: RedPajamaCommonCrawl


text:
package net.jxta.impl.endpoint;

import net.jxta.document.AdvertisementFactory;
import net.jxta.document.XMLElement;
import net.jxta.logging.Logging;
import net.jxta.peergroup.PeerGroup;
import net.jxta.protocol.PeerAdvertisement;
import net.jxta.protocol.RouteAdvertisement;

import java.util.Enumeration;
import java.util.logging.Logger;

/**
 *  Utility functions related to the Endpoint Service.
 */
public final class EndpointUtils {

    /**
     * Logger
     */
    private static final transient Logger LOG = Logger.getLogger(EndpointUtils.class.getName());

    /**
     * Extracts a route advertisement from a peer advertisement.
     *
     * @param adv The peer advertisement believed to contain a route advertisement.
     * @return The extracted route advertisement or {@code null} if a route
     * advertisement could not be extracted.
     */
    public static RouteAdvertisement extractRouteAdv(PeerAdvertisement adv) {

        try {

            // Get its EndpointService advertisement
            XMLElement endpParam = (XMLElement) adv.getServiceParam(PeerGroup.endpointClassID);

            if (endpParam == null) {
                Logging.logCheckedFine(LOG, "No Endpoint Params");
                return null;
            }

            // get the Route Advertisement element
            Enumeration paramChilds = endpParam.getChildren(RouteAdvertisement.getAdvertisementType());
            XMLElement param;

            if (paramChilds.hasMoreElements()) {
                param = (XMLElement) paramChilds.nextElement();
            } else {
                Logging.logCheckedFine(LOG, "No Route Adv in Peer Adv");
                return null;
            }

            // build the new route
            RouteAdvertisement route = (RouteAdvertisement) AdvertisementFactory.newAdvertisement(param);

            // Embedded routes often don't contain the peer id.
            route.setDestPeerID(adv.getPeerID());

            return route;

        } catch (Exception e) {

            Logging.logCheckedWarning(LOG, "failed to extract radv\n", e);

        }

        return null;
    }
}

redpajama_set_name: RedPajamaGithub


The Ecuadorian Amazon was like nowhere we've been before. It wasn't just the sight of all the different animals and plants that made the experience amazing, but the sounds and smells as well.
As we went though different areas we would smell different plants, trees and flowers. Everything was fresh yet the humidity was full on (up to 99%) at times.
The sounds of the rainforest were out of this world. All around you'd hear Cicada screeching at fluctuating volumes, birds squawking and caiman crashing around in the water.
The first time I heard Howler monkeys in the distance the unearthly noise blew my mind. It was like nothing I'd ever heard before. It's hard to explain this almost demonic low resonance of howling echoing through the trees. It's more like something from a horror film than what you'd expect from a monkey.
We managed to see so much over our five days in the jungle – even the dark night's introduced a whole new world of beasties we'd missed during the day.
Here are some of my highlights below.


The death apart from speech.
but with a savage wonder.
like finch or aspen leaf.
Patricia Nelson is a retired environmental attorney who has worked for many years with the "Activist" group of poets in the San Francisco Bay Area. Her most recent book is Spokes of Dream or Bird (Poetic Matrix Press).
Predators have vertical or round pupils. A goat's pupil is a horizontal bar that moves, enabling the goat to see far from side to side. The devil is often depicted with goat-like eyes, and separating goats from sheep is metaphor for parting the saved and damned.
the thin of dark coming under a door.
compose the mild, unblessed eye.
Goat goes into the light.
of wolf and pointed grasses.
No dazzle of light above.
with its small black dot.
Part those others from the saved.
hoofed and horned and oddly eyed.
your jaw of words reciting tall light.
that say nothing of being blessed.


We've been asked by our Spanish amigo Ricardo Patterhijo to spread the word about possible problems in Albarracin, so here goes goes.
According to Oscar Navarro, from the Asociacion Escalada Sostenible Albarracin the world renowned and world class sandstone bouldering at Albaraccin in central Spain is in serious danger of being closed.
Exacerbating the problem is Alba's higher European profile meaning more visitors, the obvious proliferation of bouldering as a sport in its own right and the fact that the site itself hosts some important cave paintings as well as lots of wildlife.
So accordingly they've issued some helpful and sensible guidelines to hopefully help enjoy the crag without it being banned.
- It's forbidden to climb at Soly y Masia.
- Tick marks - these are a big no no and causing problems - if you need to mark a hold please use a strip of tape then simply tear it off and take it away.
- Chalk please, please, please, minimize use.
- It's forbidden to climb on boulders within 30m of the tarmacced road between Albarracin and Donarque and also forbidden to climb within 30m of any site with paintings.
- It's totally forbidden to have dogs off the lead.
- As ever, please bury all human waste and ship out any paper/tissues and sanitary products.
- In any parking area with a roof/shade you must park under the roof - if your vehicle doesn't fit under the roof please parking in the main car park or in El Cabrerizo.
- It's just about Ok to spend the night in the parking area in vehicles (no tents or camping) but only if any signs of overnight stays are gone in the morning.
- Please stick to recognized paths.
- No climbing after dark or before sunrise.
- Don't make fires - it's a big dry pine forest!!!
- Smokers, take out your butts!!
So if you are going to go to Albarracin please remember the old adage - not ours but ours to look after - and remember that only a sensible and sustainable approach will help to stave off problems whether at home or abroad.


text:
Stunning 1969 home with glass conservatory can be yours for $3.6M
Erno Goldfinger designed this modern home in Surrey, England
By Lauren Ro@blauring Jan 8, 2018, 4:23pm EST
The Teesdale House was designed by Hungarian architect Erno Goldfinger in the late 1960s.
Photos via Savills
A Grade-II listed home by Hungarian architect Erno Goldfinger has come on the market in the Surrey village of Windlesham. Built for businessman Jack Perry in 1969, the Teesdale House, as it's called, is the only post-war private house in original condition by the larger-than-life modernist, who was known for his brutalist towers throughout England—and his temper. (Ian Fleming named one of his James Bond villains after him.)
In contrast to his stark concrete buildings, the 3,319-square-foot single-story home is characterized by a post-and-beam construction, timber cladding, walls of glass, and a flat roof—all arranged around a central conservatory.
The flowing layout incorporates an open-plan living-dining-kitchen area with full-height windows overlooking the countryside, an office with two glass walls, and the aforementioned glass-box conservatory, which links the common areas with the private accommodations. These include four bedrooms, one of which is an ensuite master with views.
Located on four west-facing acres, the property comes with some impressive greenery too, like, mature trees, hedges, and shrubs, and plenty of opportunities for outdoor entertaining. There's even a terraced water fountain at the front of the house; an inviting sight, to be sure. It's available through Savills for 3 million GBP, or about $3.6 million.
Via: WowHaus
Frank Lloyd Wright's Final Design, Auctioned Last Year for $1.7M, Now Asks $8M. Huh?
Tiny camper pod expands to 3 times its size in seconds The brand-new Tipoon camper can sleep a family of four, fit in most garages, and expands at a push of a button.


text:
Tulcán forced to pay $5 million to the telephone company
The amount was defined after a court ruling for improper charges, which were generated through an ordinance.
TULCÁN Editorial
The Municipality of Tulcán owes $4,942,665 to the telephone company Conecel SA, legal representative of the Claro company.
This amount, however, not related to call consumption or any of the services provided by the company, but refers to the money that the municipality of Tulcan improperly charged Conecelin the year 2014when a municipal ordinance was issued setting fees to regulate the use of public space, as well as municipal airspace, soil and subsoil, for the placement of structures, poles and the laying of networks.
Through a statement, the Mayor's Office of Tulcán reported that the amount owed began to be paid from January 2022in accordance with the judicial sentence issued in January 2018, by the District Court of Tax Litigation based in Guayaquil.
The total value, however, will be canceled throughout 36 monthsthrough monthly payments that border the $137,000which were authorized by the Municipal Council, on September 30, 2021, "without interest or fines being charged, since the year in which the Municipality of Tulcán improperly seized the money of the company Conecel SA, until today ".
unconstitutional ordinance
The lawsuit by Conecel against the Municipality of Tulcán reached the Constitutional Court in 2015.
The main challenge was against article 18 of the ordinance approved by the Municipal Council, where it was defined that natural persons, legal entities, national and foreign companies, all of them private, annually cancel new municipal taxes, generated by the installation of posts, laying networks and structures; in addition to setting the corresponding rates for the use or occupation of municipal airspace.
The rates, according to the regulations, were applied mainly to metal structures for commercial use, as antennas of all kinds, for radio, cell phones, television, but also to network lines, imposing a daily payment percentage for its installation in rural or urban areas of the canton.
For example, one of the sections imposed the 20% of the unified basic salary for antennas for cellular services, daily, stating that the payment should be for each of the antennas installed on top of the structures and that are part of the telecommunications networks, "for the concept of use of airspace".
Among other items, it was requested that radio help and amateur radio antennas pay $0.10 cents per day and commercial ones $1.50while for the network laying that belong to private companies, they wanted to subject a daily rate of $0.01 cents for each linear meter.
After being served with the lawsuit, the then mayor of TulcánJulio Cesar Robles Guevaraas well as the municipal trustee attorney of the time, Luis Marcelo Portilla Revelo, They asked that the case be dismissed and argued that Conecel, "for more than six years, has been operating in Tulcán, without reporting any benefit to the cantonwithout paying taxes, rates or contributions for the use of land".
But nevertheless, the Constitutional Court ruled against him and declared the ordinance unconstitutionalexposing, among one of the observations, that only the Central Government is responsible for charging for issues related to the communications and telecommunications spectrum, but not the municipal governments.
In addition, they mentioned that seven fixed rates within article 18 of the challenged ordinance, reached excessive values"significantly affecting the economy of private companies that, in an effort to provide the commercial service for which they were established, they see the need to make use of public space, activating the generating event of the rates regulated in the ordinance and, consequently, complying with the obligatory payment of said tax".
after those responsible
From the Syndicate Attorney of the Municipality of Tulcán It has been stated that the lawsuit against Conecel, upon assuming the current municipal administration, I was already lostso they could not do more management to avoid it.
Given this, the only procedure they did, practically, was mediate with the legal representatives of the company, so that the payment of the almost $5 million is not immediate, as was requested in the first instance, but that it can be paid in three years, with monthly installments around $137,000fulfilling the first installment in January 2022.
"It is a concern that we have expressed from the first day that we assumed the municipal administration, since we have found these sentences already lost and what we have achieved is to reach payment agreements, so as not to fall into breachesas it has been doing since 2016," said Nataly Polo, trustee of the Municipality of Tulcán, at the time.
In addition, they stated that once the payment has begun, they will initiate the corresponding procedures so that the responsible authorities are accountable for the destinations of these itemsas well as for the constant evasions to avoid disbursement of money.
"The accounts of the telephone company Conecel were seized in 2015, so they followed the judicial process so that the values can be restored, which now the Municipal GAD of Tulcán has to pay, "added Polo.
El presupuesto general del Municipio de Tulcán, anualmente, bordea los $31 millones.
Claro es la empresa que más antenas tiene a escala nacional. Hasta 2020 reportó 9.658 en todo el Ecuador, mientras que Movistar 5.054 y CNT 2.802.
Vehicle registrations increased 53% in May compared to 2021
Of the total sales, the participation of nationally manufactured models
The kidnapping of shame
embed note December 30, 2022, 4:00 AM December 30, 2022,
Covid-19: Brazil registers 37,600 new cases in 24 hours
The Ministry of Health released today (29) new figures for
Automotive sector: the challenges of the industry in 2023
2022 represented great economic challenges for all business sectors. In
The ban for the revocation of the mandate begins this Friday, February 4
Transactions with cryptocurrencies in the country must be reported
Latest from Ecuador
One more year, there is a shortage of ration cards in Cuba
"All consumers are informed that in January it will be dispatched with the same notebook from
"Over the years one learns that one should not disagree"
The only mention of Delfín Prats in the History of Cuban Literature it is a piece
The authorization for Cuban residents to stay abroad for more than 24 months remains in force
The director of Consular Affairs and Cuban Residents Abroad, Ernesto Soberón, reminded Cubans who have been
50 of the 500 Cuban doctors hired in August arrive in Italy
A first group of 50 Cuban doctors hired by Calabria last August It has arrived at
A traffic accident in Matanzas leaves at least five dead and 42 injured
(EFE).- The massive traffic accident happened this Wednesday in the municipality of Los Arabosin Matanzas, left


Industry set to see 4% CAGR over 2016-2021. Henkel completes purchase of Sun Products in major US acquisition in 2016. New product launches driven by consumer concerns regarding environmentally-friendly ingredients. 2016 sees industry post growth of 1% to reach turnover of USD90.4 billion.
This Euromonitor industrial report provides unique data and analysis of Household Cleaning and Personal Care Products in USA: ISIC 2424. With this industrial report, you'll be able to explore in detail the entire industry from its sources of supply to its buyers.


Our technical support and account management teams are dedicated to working with our current active clients to provide technical assistance for TheraOffice products. All support-related tickets are routed to our U.S.-based support associates in our main location.
Our data shows the average satisfaction rating among completed support tickets in the years 2009-2018 is 97%. Response time is typically within an hour from the time a support ticket is created and is prioritized on the severity of the issue and the order in which it was received.
Technical support is available between the hours of 8:30 AM CST to 6:00 PM CST, Monday through Friday (excluding holidays). Hands on Technology monitors messages (voice and email) left after working hours for clinic emergencies.
The most adaptable EMR in the rehabilitation space.


text:
Catalina Raffle – 29th April 2019
Comedy Night – Batyr & Smiling Mind – 3 April 2019
Golf Day 2019
Comedy Night – Wairoa School – 3 August 2018
Catalina Raffle – 3rd August 2018
Wine Raffle – 27th April 2018
Comedy Night – Emerge – 28th March 2018
Golf Day 2018 – 20 February 2018
Dinner at Mr Wong Raffle – 15th December 2017
Catalina Raffle – 27th June 2017
Comedy Night – Wairoa School – 21 June 2017
Fly & Dine Raffle – 2nd May 2017
Comedy Night – Wairoa School – 27 July 2016
Comedy Night – Laughing For Laos – 10 June 2016
Comedy Night – Eastern Respite & Recreation – 4 March 2015
Golf Day – 1st December 2016
Comedy Night – Wairoa School – 27th July 2016
Golf Day – 3rd December 2015
Comedy Night – Laughing For Laos – 10th June 2015
Comedy Night – Eastern Respite & Recreation – 4th March 2015
Smiling Mind
batyr
Eastern Respite & Recreation
Wairoa School
Laughing for Laos
Give a Little Love exists to raise much needed funds for incredibly well-deserving causes. The brain-child of Marcelo Ulvert, it began with a humble Golf Day back in 2003, and has gone on to raise over $750,000 for Cerebral Palsy Alliance, Childfund Australia, Wairoa School and Eastern Respite. With a passion for supporting causes that may not receive as much attention or funding, our mission is literally to give a little love where we can, and we sincerely thank everyone who has supported us thus far.
CURRENTLY SUPPORTING
Give a Little Love is currently focussed on hosting events that raise funds specifically to support Mental Ill-Health and Suicide Prevention in Youths, with donations to batyr and Smiling Mind.
batyr is a preventative mental health organisation, created and driven by young people, for young people. They run programs in schools and universities, with the aim of smashing the stigma around mental health and empowering young people to reach out for support. We love that they are giving a voice to young people using the power of both personal experience and shared stories.
Smiling Mind is a web and app-based meditation program to help bring mindfulness into people's lives. It has been developed by psychologists and educators and has a goal to reach 5 million young people by 2021. We love that they are integrating both learning and wellbeing to better help develop the mindful generations of the future.
"The support Cerebral Palsy Alliance has received from the Give a Little Love Foundation over many years has truly changed lives. We rely on the generosity of organisations like the Give a Little Love Foundation, and the individuals who attend their events, to help provide ongoing therapy, services and equipment to build futures for our clients and families living with cerebral palsy. We simply couldn't do this without the generosity and support of people like you doing amazing things.
Your support and involvement also raises awareness of cerebral palsy and the services Cerebral Palsy Alliance provides.
Every $1 raised for Cerebral Palsy Alliance really does make a difference. For example, Give a Little Love Foundation's support could help a child take their first steps through the use of customised walking frame; or tell their parents that they love them for the first time using a communication device. Cerebral palsy directly affects a person's ability to move. It is a lifelong permanent disability with no known cure. Every 15 hours a child in Australia is born with cerebral palsy.
That's why the Give a Little Love Foundation's support means so much to us. We cannot thank you enough."
Louise Reeson, Events & Community Fundraising AssistantCerebral Palsy Alliance
"At ChildFund Australia, we are extremely grateful for the very generous support of the Give a Little Love Foundation. Through events such as their annual golf day and comedy nights, supporters are given an opportunity to mingle with like-minded people and contribute to help children in extreme need in developing countries around the world.
Give a Little Love Foundation's events have directly supported vital projects with ChildFund. Most recently, their support enabled a maternity ward to be built and equipped in Nonghet - a remote area in Laos. Now mothers in this region can access a critically important health service, give birth in a safe environment and receive care and treatment for their newborns.
We extend our sincere gratitude and deep appreciation to the entire team at Give a Little Love Foundation for their commitment to support children who face severe disadvantage."
Nigel Spence, CEOChildFund Australia
"Eastern Respite would like to take the opportunity thank the fabulous team at Give A Little Love. Without their help and guidance we would not have been able to organise an event like the one that we held at The Comedy Store. It allowed us to introduce our organisation to locals who may not have known about the services that we provide. Through your generosity we have been able to raise over $7,300."
Bernadette Bateman, Finance ManagerEastern Respite and Recreation
"The Give a Little Love Foundation has provided outstanding support to Wairoa School over many years. This support has enabled our students to participate in high quality creative and performing arts programs at the school, fostering the development of our students as confident, creative and connected learners who are valued members of our community.
Give a Little Love Foundation events have strengthened our community by raising awareness of the school and the needs of our students. We are so appreciative of the support and generosity of the team at Give a Little Love Foundation for their commitment to the students of Wairoa School and their future."
Carmel SeetoPrincipal
Give a Little Love gives you the chance to do just that! Give…
Help us help others and join us on our incredible journey!
Give a Little Love Donate Now
Give a Little Love's donation and tax receipt processing is managed and administered by Good2Give ©Good2Give ABN 32 089 603 314.
© 2019 Give a Little Love.
Official Merchandise Sponsor


text:
class CameraSingleDragHandler: public CameraEventHandler {

public:
  CameraSingleDragHandler(bool useInertia):
  _useInertia(useInertia)
  {}

  ~CameraSingleDragHandler() {
#ifdef JAVA_CODE
  super.dispose();
#endif

  }

  bool onTouchEvent(const G3MEventContext *eventContext,
                    const TouchEvent* touchEvent,
                    CameraContext *cameraContext);

  void render(const G3MRenderContext* rc,
              CameraContext *cameraContext);

  const bool _useInertia;
  void onDown(const G3MEventContext *eventContext,
              const TouchEvent& touchEvent,
              CameraContext *cameraContext);
  void onMove(const G3MEventContext *eventContext,
              const TouchEvent& touchEvent,
              CameraContext *cameraContext);
  void onUp(const G3MEventContext *eventContext,
            const TouchEvent& touchEvent,
            CameraContext *cameraContext);
private:

  MutableVector3D _cameraPosition;
  MutableVector3D _cameraCenter;
  MutableVector3D _cameraUp;
  MutableVector2I _cameraViewPort;
  MutableMatrix44D _cameraModelViewMatrix;
  MutableVector3D _finalRay;
};

#endif