Local structure co-occurrence pattern for image retrieval

Ke Zhang; Fan Zhang; Jia Lu; Yinghua Lu; Jun Kong; Ming Zhang

doi:10.1117/1.JEI.25.2.023030

28 April 2016 Local structure co-occurrence pattern for image retrieval

Ke Zhang, Fan Zhang, Jia Lu, Yinghua Lu, Jun Kong, Ming Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 2, 023030 (April 2016). https://doi.org/10.1117/1.JEI.25.2.023030

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.

Citation Download Citation

Ke Zhang, Fan Zhang, Jia Lu, Yinghua Lu, Jun Kong, and Ming Zhang "Local structure co-occurrence pattern for image retrieval," Journal of Electronic Imaging 25(2), 023030 (28 April 2016). https://doi.org/10.1117/1.JEI.25.2.023030

Published: 28 April 2016

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