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Local discriminative regions play important roles in finegrained image analysis tasks. How to locate local discriminative regions with only category label and learn discriminative representation from these regions have been hot spots. In our work, we propose Searching Discriminative Regions (SDR) and Learning Discriminative Regions (LDR) method to search and learn local discriminative regions in images. The SDR method adopts attention mechanism to iteratively search for highresponse regions in images, and uses this as a clue to locate local discriminative regions. Moreover, the LDR method is proposed to learn compact within category and sparse between categories representation from the raw image and local images. Experimental results show that our proposed approach achieves excellent performance in both fine-grained image retrieval and classification tasks, which demonstrates its effectiveness.

Extracting discriminative local features has attracted many research focus in fine-grained image retrieval task. With attention mechanism and softmax-like loss functions, deep neural networks could locate and learn the representation of the most discriminative region of objects, however, which also makes other non-most discriminative regions be ignored to some extent. In our work, to extract more local features, we propose a method that could proposes multiple discriminative regions on different scales, which could provide more refined local and multi-sacle representation for fine-grained image retrieval. Experimental results show that our proposed method achieves excellent performance on two benchmark fine-grained datasets, which demonstrates its effectiveness.