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Journal of Electronic Imaging

Local structure co-occurrence pattern for image retrieval
Author(s): Ke Zhang; Fan Zhang; Jia Lu; Yinghua Lu; Jun Kong; Ming Zhang
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Paper Abstract

Image description and annotation is an active research topic in content-based image retrieval. How to utilize human visual perception is a key approach to intelligent image feature extraction and representation. This paper has proposed an image feature descriptor called the local structure co-occurrence pattern (LSCP). LSCP extracts the whole visual perception for an image by building a local binary structure, and it is represented by a color-shape co-occurrence matrix which explores the relationship of multivisual feature spaces according to visual attention mechanism. As a result, LSCP not only describes low-level visual features integrated with texture feature, color feature, and shape feature but also bridges high-level semantic comprehension. Extensive experimental results on an image retrieval task on the benchmark datasets, corel-10,000, MIT VisTex, and INRIA Holidays, have demonstrated the usefulness, effectiveness, and robustness of the proposed LSCP.

Paper Details

Date Published: 28 April 2016
PDF: 13 pages
J. Electron. Imag. 25(2) 023030 doi: 10.1117/1.JEI.25.2.023030
Published in: Journal of Electronic Imaging Volume 25, Issue 2
Show Author Affiliations
Ke Zhang, Northeast Normal Univ. (China)
Fan Zhang, Northeast Normal Univ. (China)
Jia Lu, Northeast Normal Univ. (China)
Yinghua Lu, Northeast Normal Univ. (China)
Jun Kong, Northeast Normal Univ. (China)
Ming Zhang, Northeast Normal University (China)

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