By Topic

An improved retrieval performance with hybrid shape descriptor and feature matching

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Anuar, F.M. ; Fac. of Eng., Multimedia Univ., Cyberjaya, Malaysia ; Fauzi, M.F.A. ; Mansor, S.

Research on Content Based Image Retrieval (CBIR) has become popular as it offers solutions to overcome or complement the drawbacks of Text Based Image Retrieval (TBIR). In CBIR, feature extraction and feature matching are two critical processes, which are of high importance to the retrieval performance of the system. This paper introduces a new approach to shape-based image retrieval by combining global and local shape features using Zernike moments (ZM) and edge-gradient co-occurrence matrix (EGCM) respectively. Two-stage matching strategy is then used to measure similarity between images. Our proposed method achieves higher precision rate compared to other commonly used shape feature.

Published in:

Image and Vision Computing New Zealand (IVCNZ), 2010 25th International Conference of

Date of Conference:

8-9 Nov. 2010