You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We perform classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends. Most of these coins are eroded due to their age and varying degrees of preservation, thereby affecting their informative attributes for visual recognition. Changes in the positions of principal symbols on the reverse motifs also cause huge variations among the coin types. Lastly, in-plane orientations, uneven illumination, and a moderate background clutter further make the task of classification non-trivial and challenging. To this end, we present a novel network model, CoinNet, that employs compact bilinear pooling, residual groups, and feature attention layers. Furthermore, we gathered the largest and most diverse image dataset of the Roman Republican coins that contains more than 18,000 images belonging to 228 different reverse motifs. On this dataset, our model achieves a classification accuracy of more than and outperforms the conventional bag-of-visual-words based approaches and more recent state-of-the-art deep learning methods. We also provide a detailed ablation study of our network and its generalization capability.

Cited Abstract(s):
[1]: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

[2]: 

[3]: In this paper we address the problem of building a local image descriptor that is insensitive to the complex appearance changes induced by illumination variations on non-flat objects. The presented descriptor is based on multi-scale and multi-oriented even Gabor filters and constructed in such a way that typical effects of illumination variations like changes of edge polarity or spatially varying brightness changes are taken into account for illumination insensitivity. For evaluation, a dataset of textured as well as textureless objects is used which introduces a greater challenge towards evaluating the robustness against illumination changes than conventional datasets used in the past. The experiments finally show the superiority of our descriptor compared to existing ones under illumination changes.

[4]: Numismatics deals with various historical aspects of the phenomenon money. Fundamental part of a numismatists work is the identification and classification of coins according to standard reference books. The recognition of ancient coins is a highly complex task that requires years of experience in the entire field of numismatics. To date, no optical recognition system for ancient coins has been investigated successfully. In this paper, we present an extension and combination of local image descriptors relevant for ancient coin recognition. Interest points are detected and their appearance is described by local descriptors. Coin recognition is based on the selection of similar images based on feature matching. Experiments are presented for a database containing ancient coin images demonstrating the feasibility of our approach.

[5]: 

[6]: In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [Mikolajczyk, K and Schmid, C, 2004]. Many different descriptors have been proposed in the literature. It is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [Belongie, S, et al, April 2002], steerable filters [Freeman, W and Adelson, E, Setp. 1991], PCA-SIFT [Ke, Y and Sukthankar, R, 2004], differential invariants [Koenderink, J and van Doorn, A, 1987], spin images [Lazebnik, S, et al, 2003], SIFT [Lowe, D. G., 1999], complex filters [Schaffalitzky, F and Zisserman, A, 2002], moment invariants [Van Gool, L, et al, 1996], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.

[7]: In this paper, we build upon the idea of using robust dense correspondence estimation for exemplar-based image classification and adapt it to the problem of ancient coin classification. We thus account for the lack of available training data and demonstrate that the matching costs are a powerful dissimilarity metric to establish coin classification for training set sizes of one or two images per class. This is accomplished by using a flexible dense correspondence search which is highly insensitive to local spatial differences between coins of the same class and different coin rotations between images. Additionally, we introduce a coarse-to-fine classification scheme to decrease runtime which would be otherwise linear to the number of classes in the training set. For evaluation, a new dataset representing 60 coin classes of the Roman Republican period is used. The proposed system achieves a classification rate of 83.3 and a runtime improvement of 93 through the coarse-to-fine classification.

[8]: Classification of ancient coins is a substantial part of numismatic research which needs a large amount of expert knowledge due to the high number of classes to be considered. In this paper we propose an automatic image-based classification method for ancient coins to support this time-consuming and difficult process. We demonstrate that previously proposed learning-based methods suffer from the practical conditions of this problem: a high number of classes, limited number of training samples per class and complex intra-class variations. As a solution we propose a similarity metric based on feature correspondence which is designed to be robust against the possible intra-class coin variations like degraded parts, non-rigid deformations and illumination-induced appearance changes. The similarity metric is used in an exemplar-based ancient coin classification scheme which shows to outperform previously proposed methods for ancient coin recognition. Experiments are conducted on a dataset of 60 Roman Republican coin classes where the presented method achieves classification rates ranging from 72.7 for the case of one training sample per class up to 97.2 when nine training samples per class are used.
The first exclusive method for ancient coins [4] uses a combination of local feature descriptors [1] [2] [6] to perform an exemplar-based classification. Zambanini and Kampel [7] apply dense correspondence-based feature matching called SIFT flow [5] . To improve the quality of local features matching, Zambanini al [8] employ the geometric consistency of the matched features. Similarly, a more customized descriptor for ancient coin classification called Local Image Descriptor Robust to Illumination Changes (LIDRIC) [3] is proposed to alleviate the sensitivity to illumination changes. To sum up, in the absence of training data, the feature matching-based methods achieve acceptable classification rates. However, they are not easily scalable to more extensive datasets. They disregard the inherent domain-specific knowledge, which is extremely important from a numismatics perspective to make the classification task complaint with the standard reference books in this subject.