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Proceedings Paper

Feature hashing for fast image retrieval
Author(s): Lingyu Yan; Jiarun Fu; Hongxin Zhang; Lu Yuan; Hui Xu
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Paper Abstract

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

Paper Details

Date Published: 8 March 2018
PDF: 6 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060903 (8 March 2018); doi: 10.1117/12.2282198
Show Author Affiliations
Lingyu Yan, Hubei Univ. of Technology (China)
Jiarun Fu, Hubei Univ. of Technology (China)
Hongxin Zhang, Hubei Univ. of Technology (China)
Lu Yuan, Hubei Univ. of Technology (China)
Hui Xu, Hubei Univ. of Technology (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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