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

Tools and techniques for color image retrieval
Proceedings of SPIE (March 12 1996)
Multimedia search engine with relevance feedback
Proceedings of SPIE (December 19 2001)
Spatially organized visualization of image query results
Proceedings of SPIE (February 10 2011)
Image retrieval with multiresolution color space quantization
Proceedings of SPIE (September 29 1996)

Back to Top