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The content based image retrieval aims to find the similar images from a large scale dataset against a query image.
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P. Wu, S. C. Hoi, H. Xia, P. Zhao, D. Wang, and C. Miao, “Online multimodal deep similarity learning with application to image retrieval,” in
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J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, “Deep learning for content-based image retrieval: A comprehensive study,” in
2014
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S. R. Dubey, S. K. Singh, and R. K. Singh, “Local wavelet pattern: a new feature descriptor for image retrieval in medical ct databases,”
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F. Shen, C. Shen, W. Liu, and H. Tao Shen, “Supervised discrete hashing,” in
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M. A. Carreira-Perpinán and R. Raziperchikolaei, “Hashing with binary autoencoders,” in
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Y. Pan, T. Yao, H. Li, C.-W. Ngo, and T. Mei, “Semi-supervised hashing with semantic confidence for large scale visual search,” in
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K. Lin, H.-F. Yang, K.-H. Liu, J.-H. Hsiao, and C.-S. Chen, “Rapid clothing retrieval via deep learning of binary codes and hierarchical search,” in
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A. Babenko and V. Lempitsky, “Aggregating local deep features for image retrieval,” in
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J. Yue-Hei Ng, F. Yang, and L. S. Davis, “Exploiting local features from deep networks for image retrieval,” in
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T. Uricchio, M. Bertini, L. Seidenari, and A. Bimbo, “Fisher encoded convolutional bag-of-windows for efficient image retrieval and social image tagging,” in
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S. R. Dubey, S. K. Singh, and R. K. Singh, “Multichannel decoded local binary patterns for content-based image retrieval,”
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J. Yu, X. Yang, F. Gao, and D. Tao, “Deep multimodal distance metric learning using click constraints for image ranking,”
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2016
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C. Huang, C. Change Loy, and X. Tang, “Unsupervised learning of discriminative attributes and visual representations,” in
2016
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2016
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2016
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B. Zhuang, G. Lin, C. Shen, and I. Reid, “Fast training of triplet-based deep binary embedding networks,” in
2016
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T. Yao, F. Long, T. Mei, and Y. Rui, “Deep semantic-preserving and ranking-based hashing for image retrieval,” in
2016
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V. Kumar BG, G. Carneiro, and I. Reid, “Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions,” in
2016
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2016
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2018
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