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Neurocomputing v.229, 2017년, pp.45 - 53   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Large-scale image retrieval with supervised sparse hashing

Xu, Yan (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, PR China ); Shen, Fumin (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, PR China ); Xu, Xing (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, PR China ); Gao, Lianli (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, PR China ); Wang, Yuan (Department of Industrial and Systems Engineering, National University of Singapore, Singapore ); Tan, Xiao (The University of Hong Kong, Hong Kong );
  • 초록  

    Abstract 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.


  • 주제어

    Learning based hashing .   Medical .   Sparsity .   Image retrieval.  

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