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Graph regularized supervised cross-view hashing

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Abstract

Hashing methods have received significant attention for effective and efficient large scale similarity search in computer vision and information retrieval community. However, most existing cross-view hashing methods mainly focus on either similarity preservation of data or cross-view correlation. In this paper, we propose a graph regularized supervised cross-view hashing (GSCH) to preserve both the semantic correlation and the intra-view and inter view similarity simultaneously. In particular, GSCH uses intra-view similarity to estimate inter-view similarity structure. We further propose a sequential learning approach to derive the hashing function for each view. Experimental results on benchmark datasets against state-of-the-art methods show the effectiveness of our proposed method.

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Acknowledgements

The authors would like to thank the anonymous referees for their constructive suggestions and comments. This work was supported by the National Natural Science Foundation of China (Grants No. 61602248), the Fundamental Research Funds for the Central Universities (Grants No.KYZ201549) and the Natural Science Foundation of Jiangsu Province(Grants No. BK20160741).

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  1. College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China

    Xin Shu, Haiyan Jiang & Huanliang Xu

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  1. Xin Shu
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  2. Haiyan Jiang
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  3. Huanliang Xu
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Correspondence to Xin Shu.

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Shu, X., Jiang, H. & Xu, H. Graph regularized supervised cross-view hashing. Multimed Tools Appl 77, 28207–28224 (2018). https://doi.org/10.1007/s11042-018-5988-3

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