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Volume: 30 | Article ID: art00005
Learn a Hybrid Collaborative Representation for Fine-Grained Image Classification
  DOI :  10.2352/ISSN.2470-1173.2018.10.IMAWM-339  Published OnlineJanuary 2018

Image classification has attracted more and more interest over the recent years. Consequently, a number of excellent non-parametric classification algorithms, such as collaborative representation based classification (CRC), have emerged and achieved superior performance to parametric classification algorithms. However, for fine-grained image classification task, both the class specific attributes and the shared attributes play significant roles in describing the image. CRC scheme does not consider the characteristics and merely utilizes all attributes without separation to represent an image. In this paper, we propose a hybrid collaborative representation based classification method to describe an image from perspective of the shared features, as well as the class specific features. Moreover, to reduce the representation error and obtain precise description, we learn a dictionary for hybrid collaborative representation with the training samples. We conduct extensive experiments on fine-grained image datasets to verify the superior performance of our proposed algorithm compared with the conventional approaches.

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Wen-Yang Xie, Bao-Di Liu, Xue Li, Yan-Jiang Wang, "Learn a Hybrid Collaborative Representation for Fine-Grained Image Classificationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2018,  pp 339-1 - 339-6,

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Electronic Imaging
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