Regular Articles

Local structure co-occurrence pattern for image retrieval

[+] Author Affiliations
Ke Zhang, Fan Zhang, Jia Lu, Yinghua Lu, Jun Kong, Ming Zhang

Northeast Normal University, School of Computer Science and Information Technology, Changchun 130117, China

Northeast Normal University, Key Laboratory of Intelligent Information Processing, Changchun 130117, China

J. Electron. Imaging. 25(2), 023030 (Apr 28, 2016). doi:10.1117/1.JEI.25.2.023030
History: Received November 19, 2015; Accepted March 30, 2016
Text Size: A A A

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

Citation

Ke Zhang ; Fan Zhang ; Jia Lu ; Yinghua Lu ; Jun Kong, et al.
"Local structure co-occurrence pattern for image retrieval", J. Electron. Imaging. 25(2), 023030 (Apr 28, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.2.023030


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.