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Graph regularized supervised cross-view hashing
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DOI：10.1007/s11042-018-5988-3.png)
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Abstract
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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.
Keywords:
Hashing
Cross-view retrieval
Spectral relaxation
Sequential learning
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