Fine-Grained Image Classification by Class and Image-Specific Decomposition With Multiple Views

Chunjie Zhang, Huihui Bai, Yao Zhao

Abstract: Fine-grained image classification attempts to accurately classify images that are similar to each other. Multiview information is often used to improve the classification accuracy. Although great progress has been made, fine-grained image classification methods still have two drawbacks. On the one hand, they often treat each image independently without considering image correlations within the same class along with the distinctive characters of each image. On the other hand, multiview correlations are often used during classifier training, leaving the correlations of different views unconsidered. To solve these two problems, in this paper, we propose a novel fine-grained image classification method by class and image-specific decomposition with multiviews (CISD-MV). For each view, we treat images of the same class jointly by decomposing the class and image-specific information. Since images of different classes are similar and correlated, we linearly model class correlations of images using decomposed low-rank parts. In addition, for each image, the representations of different views are correlated, and we use linear transformation to model view correlations. We jointly optimise for the class and image-specific components along with the class correlation and view correlation transformation matrixes. A testing image is assigned to the class that has the minimum summed reconstruction error. We conduct fine-grained image classification experiments on several public fine-grained image datasets. Experimental results and analysis show the effectiveness of the proposed method.