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Complex Zernike Moments Features for Shape-Based Image Retrieval
Shan Li1; Moon-Chuen Lee1; Chi-Man Pun2
2008-12-17
Source PublicationIEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS
ISSN1083-4427
Volume39Issue:1Pages:227-237
Abstract

Shape is a fundamental image feature used in content-based image-retrieval systems. This paper proposes a robust and effective shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments (ZMs). As the rotation of an image has an impact on the ZM phase coefficients of the image, existing proposals normally use magnitude-only ZM as the image feature. In this paper, we compare, by using a mathematical form of analysis, the amount of visual information captured by ZM phase and the amount captured by ZM magnitude. This analysis shows that the ZM phase captures significant information for image reconstruction. We therefore propose combining both the magnitude and phase coefficients to form a new shape descriptor, referred to as invariant ZM descriptor (IZMD). The scale and translation invariance of IZMD could be obtained by prenormalizing the image using the geometrical moments. To make the phase invariant to rotation, we perform a phase correction while extracting the IZMD features. Experiment results show that the proposed shape feature is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. The proposed IZMD feature also outperforms the commonly used magnitude-only ZMD in terms of noise robustness and object discriminability.

KeywordInvariant Features Object Recognition Phase Shape Zernike Moments (Zms)
DOI10.1109/TSMCA.2008.2007988
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Theory & Methods
WOS IDWOS:000262429600021
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Citation statistics
Cited Times [WOS]:110   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorChi-Man Pun
Affiliation1.Chinese University of Hong Kong, Shatin, Hong Kong
2.University of Macau, Taipa, Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Shan Li,Moon-Chuen Lee,Chi-Man Pun. Complex Zernike Moments Features for Shape-Based Image Retrieval[J]. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS,2008,39(1):227-237.
APA Shan Li,Moon-Chuen Lee,&Chi-Man Pun.(2008).Complex Zernike Moments Features for Shape-Based Image Retrieval.IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS,39(1),227-237.
MLA Shan Li,et al."Complex Zernike Moments Features for Shape-Based Image Retrieval".IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS 39.1(2008):227-237.
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