You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey 
Chapter Title:  Fisher Vector Encoding 
1. Abstract of Cited Paper (BIB001):  Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification performance superior to that of the model based approaches. An ideal classifier should combine these two complementary approaches. In this paper, we develop a natural way of achieving this combination by deriving kernel functions for use in discriminative methods such as support vector machines from generative probability models. We provide a theoretical justification for this combination as well as demonstrate a substantial improvement in the classification performance in the context of DNA and protein sequence analysis. 
2. Abstract of Cited Paper (BIB002):  Several state-of-the-art Generic Visual Categorization (GVC) systems are built around a vocabulary of visual terms and characterize images with one histogram of visual word counts. We propose a novel and practical approach to GVC based on a universal vocabulary, which describes the content of all the considered classes of images, and class vocabularies obtained through the adaptation of the universal vocabulary using class-specific data. An image is characterized by a set of histograms - one per class - where each histogram describes whether the image content is best modeled by the universal vocabulary or the corresponding class vocabulary. It is shown experimentally on three very different databases that this novel representation outperforms those approaches which characterize an image with a single histogram. 
3. Abstract of Cited Paper (BIB003):  Within the field of pattern classification, the Fisher kernel is a powerful framework which combines the strengths of generative and discriminative approaches. The idea is to characterize a signal with a gradient vector derived from a generative probability model and to subsequently feed this representation to a discriminative classifier. We propose to apply this framework to image categorization where the input signals are images and where the underlying generative model is a visual vocabulary: a Gaussian mixture model which approximates the distribution of low-level features in images. We show that Fisher kernels can actually be understood as an extension of the popular bag-of-visterms. Our approach demonstrates excellent performance on two challenging databases: an in-house database of 19 object/scene categories and the recently released VOC 2006 database. It is also very practical: it has low computational needs both at training and test time and vocabularies trained on one set of categories can be applied to another set without any significant loss in performance. 
4. Abstract of Cited Paper (BIB004):  The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speed-up the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach. 
5. Abstract of Cited Paper (BIB005):  The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we demonstrate state-of-the-art accuracy on CalTech 256. A major advantage is that these results are obtained using only SIFT descriptors and costless linear classifiers. Equipped with this representation, we can now explore image classification on a larger scale. In the second part, as an application, we compare two abundant resources of labeled images to learn classifiers: ImageNet and Flickr groups. In an evaluation involving hundreds of thousands of training images we show that classifiers learned on Flickr groups perform surprisingly well (although they were not intended for this purpose) and that they can complement classifiers learned on more carefully annotated datasets. 
6. Abstract of Cited Paper (BIB006):  Textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. However, the selection of informative ones with highly discriminatory ability to improve the classification accuracy is still one of the well-known problems in remote sensing. In this paper, we propose a new method based on the Gaussian mixture model (GMM) in calculating Shannon's mutual information between multiple features and the output class labels. We apply this, in a real context, to a textural feature selection algorithm for multispectral image classification so as to produce digital thematic maps for cartography exploitation. The input candidate features are extracted from an HRV-XS SPOT image of a forest area in Rabat, Morocco, using wavelet packet transform (WPT) and the gray level cooccurrence matrix (GLCM). The retained classifier is the support vectors machine (SVM). The results show that the selected textural features, using our proposed method, make the largest contribution to improve the classification accuracy than the selected ones by mutual information between individual variables. The use of spectral information only leads to poor performances. 
7. Abstract of Cited Paper (BIB007):  In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We show that the popular iterative closest point (ICP) method and several existing point set registration methods in the field are closely related and can be reinterpreted meaningfully in our general framework. Our instantiation of this general framework is based on the the L2 distance between two Gaussian mixtures, which has the closed-form expression and in turn leads to a computationally efficient registration algorithm. The resulting registration algorithm exhibits inherent statistical robustness, has an intuitive interpretation, and is simple to implement. We also provide theoretical and experimental comparisons with other robust methods for point set registration. 
8. Abstract of Cited Paper (BIB008):  To model a scene for background subtraction, Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. However, GMM often suffers from a tradeoff between robustness to background changes and sensitivity to foreground abnormalities and is inefficient in managing the tradeoff for various surveillance scenarios. By reviewing the formulations of GMM, we identify that such a tradeoff can be easily controlled by adaptive adjustments of the GMM's learning rates for image pixels at different locations and of distinct properties. A new rate control scheme based on high-level feedback is then developed to provide better regularization of background adaptation for GMM and to help resolving the tradeoff. Additionally, to handle lighting variations that change too fast to be caught by GMM, a heuristic rooting in frame difference is proposed to assist the proposed rate control scheme for reducing false foreground alarms. Experiments show the proposed learning rate control scheme, together with the heuristic for adaptation of over-quick lighting change, gives better performance than conventional GMM approaches. 
9. Abstract of Cited Paper (BIB009):  A general framework for solving image inverse problems with piecewise linear estimations is introduced in this paper. The approach is based on Gaussian mixture models, which are estimated via a maximum a posteriori expectation-maximization algorithm. A dual mathematical interpretation of the proposed framework with a structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques. We demonstrate that, in a number of image inverse problems, including interpolation, zooming, and deblurring of narrow kernels, the same simple and computationally efficient algorithm yields results in the same ballpark as that of the state of the art. 
10. Abstract of Cited Paper (BIB010):  A standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors, encode them into a high dimensional vector and pool them into an image-level signature. The most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements. This leads to the popular Bag-of-Visual words representation. In this work, we propose to use the Fisher Kernel framework as an alternative patch encoding strategy: we describe patches by their deviation from an “universal” generative Gaussian mixture model. This representation, which we call Fisher vector has many advantages: it is efficient to compute, it leads to excellent results even with efficient linear classifiers, and it can be compressed with a minimal loss of accuracy using product quantization. We report experimental results on five standard datasets—PASCAL VOC 2007, Caltech 256, SUN 397, ILSVRC 2010 and ImageNet10K—with up to 9M images and 10K classes, showing that the FV framework is a state-of-the-art patch encoding technique.

Literature Review:
Based on the Fisher Kernel framework BIB002 , the Fisher Vector BIB005 BIB004 BIB006 BIB011 is an extension of the bag-of-visual-words (BOV) and encodes feature vectors of patches within one specific image by a gradient vector derived from log likelihood of a universal generative Gaussian mixture model (GMM)  BIB010 BIB008 BIB007  BIB009 which can be regarded as a probabilistic visual vocabulary. Let X = {x t |1 ≤ t ≤ T} be the set of T local low level D dimensional feature vectors extracted from an image, e.g., a set of 128 dimensional SIFT feature vectors of interest patches in the image, and is the probability density function of the GMM, where Θ = {w, µ, Σ} = {w i , µ i , Σ i |1 ≤ i ≤ N} are parameters, w i ∈ R are the mixture weights with ++ are the D × D symmetric positive definite covariance matrices which are assumed to be diagonal by the variance vector σ 2 i = diag Σ (i) , and g(x t µ (i) , Σ (i) ) are the component Gaussian density functions. Then, the fisher vector encoding adopted in BIB006 follows steps of: (1) GMM parameters Θ are trained on a large number of images using the expectation-maximization (EM)-based maximum likelihood estimate (MLE) of BIB003  BIB001 . (2) Both Φ norm The final fisher vector Φ norm (X) of the image X is the concatenation of Φ norm Thus, the dimension of the fisher vector Φ norm (X) is 2ND. Furthermore, the authors stated that, linear classifiers provide almost the same results as computation expensive kernel classifiers by using the fisher vector encoding which yields high-classification accuracy and is efficient for large scale processing.