A Comparative Study of Fourier Descriptors for Shape Representation and Retrieval

Dengsheng Zhang and Guojun Lu
Gippsland School of Computing and Information Technology
Monash University
Churchill, Victoria 3842
Australia

Tel: 61-3-9902 6772
Fax: 61-3-9902 6842
Email: dengsheng.zhang, guojun.lu@infotech.monash.edu.au

ABSTRACT

Shape is one of the primary low level image features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exist. However, most of those methods either do not well capture shape features or are difficult to do normalization (making matching hard). Among them, methods based Fourier descriptors (FDs) achieve both good representation (perceptually meaningful) and easy normalization. Besides, FDs are easy to derive and compact in terms of representation. Design of FDs focuses on how to derive Fourier invariants from Fourier coefficients and how to obtain Fourier coefficients from shape signatures. Different Fourier invariants and shape signatures have been exploited to derive FDs. In this paper, we study different FDs and build a Java retrieval framework to compare shape retrieval performance using different FDs in terms of computation complexity, robustness, convergence speed and retrieval performance. The retrieval performance of the different FDs is compared using a standard shape database. (full paper)

Key words:CBIR, Shape, Fourier descriptors, Retrieval.