Regular Articles

Local spectral method to seeded image cosegmentation

[+] Author Affiliations
Qinghua Liang

Beijing Jiaotong University, Institute of Information Science, No. 3 Shang Yuan Cun, Beijing 100044, China

Beijing Key Laboratory of Advanced Information Science and Network Technology, No. 3 Shang Yuan Cun, Beijing 100044, China

Zhenjiang Miao

Beijing Jiaotong University, Institute of Information Science, No. 3 Shang Yuan Cun, Beijing 100044, China

Beijing Key Laboratory of Advanced Information Science and Network Technology, No. 3 Shang Yuan Cun, Beijing 100044, China

J. Electron. Imaging. 23(2), 023018 (Apr 09, 2014). doi:10.1117/1.JEI.23.2.023018
History: Received October 23, 2013; Revised February 26, 2014; Accepted March 12, 2014
Text Size: A A A

Abstract.  The cosegmentation problem is referred to as segmenting the same or similar objects simultaneously from a group of images. However, designing a robust and efficient cosegmentation algorithm is a challenging work because of the variety and complexity of the object and the background. We proposed a new seeded image cosegmentation method based on a local spectral method, which combines bottom-up information and seeds’ knowledge effectively for segmentation. Multiple images are connected into a weighted undirected graph so the cosegmentation problem is converted into a graph partitioning problem that is solved by biased normalized cuts. The results of the cosegmentation experiment reveal that the proposed method performs well even in the presence of some noise images (images not containing a common object) or in the condition of the image set containing more than one object.

Figures in this Article
© 2014 SPIE and IS&T

Citation

Qinghua Liang and Zhenjiang Miao
"Local spectral method to seeded image cosegmentation", J. Electron. Imaging. 23(2), 023018 (Apr 09, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.2.023018


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

PubMed Articles
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.