Large-scale image retrieval with supervised sparse hashing

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• 作者：Yan Xu^a ; ^{xuyan5533@gmail.com}Author Vitae ; Fumin Shen^a ; ^b ; ^{fumin.shen@gmail.com}Author Vitae ; Xing Xu^a ; ^{xing.xu@uestc.edu.cn}Author Vitae ; Lianli Gao^a ; ^{lianli.gao@uestc.edu.cn}Author Vitae ; Yuan Wang^c ; ^{iseway@nus.edu.sg}Author Vitae ; Xiao Tan^d ; ^{tanxchong@gmail.com}Author Vitae
• 关键词：Learning based hashing ; Medical ; Sparsity ; Image retrieval
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：15 March 2017
• 年：2017
• 卷：229
• 期：Complete
• 页码：45-53
• 全文大小：608 K
• 卷排序：229

文摘

In recent years, learning based hashing becomes an attractive technique in large-scale image retrieval due to its low storage and computation cost. Hashing methods map each high-dimensional vector onto a low-dimensional hamming space by projection operators. However, when processing high dimensional data retrieval, many existing methods including hashing cost a majority of time on projection operators. In this paper, we solve this problem by implementing a sparsity regularizer. On one hand, due to the sparse property of the projection matrix, our method effectively lower both the storage and computation cost. On the other hand, we reduce the effective number of parameters involved in the learned projection matrix according to sparsity regularizer, which helps avoid overfitting problem. Without relaxing binary constraints, an iterative scheme jointly optimizing the objective function directly was given, which helps to obtain effective and efficient binary codes. We evaluate our method on three databases and compare it with some state-of-the-art hashing methods. Experimental results demonstrate that our method outperforms the comparison approaches.