Abstract
In this paper, we propose a deep siamese convolutional neutral network (DSCNN) to learn semantic-preserved global-level and local-level hashing codes simultaneously for effective image retrieval. Particularly, we analyze the visual attention characteristic inside hash bits by activation map of deep convolutional feature and propose a novel approach of bit selecting to reinforce the pertinence of local-level code. Finally, unlike most existing retrieval methods which use global or unsupervised local descriptors separately, leading to unexpected precision, we present a multi-level hash search method, taking advantage of both local and global properties of deep features. The experimental results show that our method outperforms several state-of-the-art on the Oxford 5k/105k and Paris 6k datasets.
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Acknowledgements
This work is supported by National Science Foundation of China (61373060,61672280), Qing Lan Project and the Research Foundation of ZTE Corporation.
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Dong, Z., Song, G., Jia, X., Tan, X. (2016). Deep Multi-level Hashing Codes for Image Retrieval. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_11
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DOI: https://doi.org/10.1007/978-981-10-3476-3_11
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