28 April 2016 Local structure co-occurrence pattern for image retrieval
Author Affiliations +
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.
© 2016 SPIE and IS&T
Ke Zhang, Fan Zhang, Jia Lu, Yinghua Lu, Jun Kong, 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 . Submission:
JOURNAL ARTICLE
13 PAGES


SHARE
Back to Top