Image Retrieval Algorithm Based on Sparse Reconstruction Coding

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Image Retrieval Algorithm Based on Sparse Reconstruction Coding

Journal Title: 河南科技大学学报（自然科学版） - Year 2018, Vol 39, Issue 3

Abstract

Because the sparse structure of data was not considered in the construction of hash function by the existing methods, a hash function learning method based on sparse reconstruction was proposed. l21 norm of the similar point was used to sparsely restrain reconstruct coefficient to enhance the discriminat property in the process of locality preserving mapping. The Laplace matrix was used to constrain the local neighborhood relation. A balance mechanism between the covariance matrix and the reconstruction error of minimize data was established. The inherent requirements and constraints in the process of hash function construction were considered and integrated from the perspective of the feature space and the mapped Hamming space. A public image retrieval data set Caltech-256 was used to test. The experimental results show that when the encoding length is 32 bit, the retrieval accuracy of the proposed algorithm is 4. 69% higher than that of other unsupervised deep hashing algorithm.

Authors and Affiliations

Penghui HU, Huaping ZHU, Chunyan WANG

Keywords

hash function; image retrieval; sparse reconstruction; sparse retrieval; hamming space;

EP ID EP464884
DOI -
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