Abstract
Approximate Nearest Neighbors (ANN) Search has attracted much attention in recent years. Hashing is a promising way for ANN which has been widely used in large-scale image retrieval tasks. However, most of the existing hashing methods are designed for single-labeled data. On multi-labeled data, those hashing methods take two images as similar if they share at least one common label. But this way cannot preserve the order relations in multi-labeled data. Meanwhile, most hashing methods are based on hand-crafted features which are costing. To solve the two problems above, we proposed a novel supervised hashing method to perform hash codes learning for multi-labeled data. In particular, we firstly extract the order-preserving data features through deep convolutional neural network. Secondly, the order-preserving features would be used for learning hash codes. Extensive experiments on two real-world public datasets show that the proposed method outperforms state-of-the-art baselines in the image retrieval tasks.
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Acknowledgement
This work was supported by 863 Program (2015AA015404), 973 Program (2013CB329303), China National Science Foundation (61402036, 60973083, 61273363), Beijing Advanced Innovation Center for Imaging Technology (BAICIT-2016007).
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Wang, D., Huang, H., Lin, HK., Mao, XL. (2017). Supervised Hashing for Multi-labeled Data with Order-Preserving Feature. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_2
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