ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (8): 1707-1714.doi: 10.7544/issn1000-1239.2020.20200122

Special Issue: 2020数据挖掘与知识发现专题

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Mutual Linear Regression Based Supervised Discrete Cross-Modal Hashing

Liu Xingbo1, Nie Xiushan2, Yin Yilong1   

  1. 1(School of Software, Shandong University, Jinan 250101);2(School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101)
  • Online:2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61671274, 61876098, 61701280, 61573219), the National Key Research and Developinent Program of China (2018YFC0830100, 2018YFC0830102), the China Postdoctoral Science Foundation (2016M592190), and the Special Funds for Distinguished Professors of Shandong Jianzhu University.

Abstract: Cross-modal hashing can map heterogeneous multimodal data into compact binary codes with similarity preserving, which provides great efficiency in cross-modal retrieval. Existing cross-modal hashing methods usually utilize two different projections to describe the correlation between Hash codes and class labels. In order to capture the relation between Hash codes and semantic labels efficiently, we propose a method named mutual linear regression based supervised discrete cross-modal hashing(SDCH) in this study. Only one stable projection is used in the proposed method to describe the linear regression relation between Hash codes and corresponding labels, which enhances the precision and stability in cross-modal hashing. In addition, we learn the modality-specific projections for out-of-sample extension by preserving the similarity and considering the feature distribution with different modalities. Comparisons with several state-of-the-art methods on two benchmark datasets verify the superiority of SDCH under various cross-modal retrieval scenarios.

Key words: approximate nearest neighbour search, cross-modal retrieval, learning-based hashing, supervised hashing, mutual linear regression

CLC Number: