Farthest point distance: A new shape signature for Fourier descriptors
Image Communication
MADE: a composite visual-based 3D shape descriptor
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Invariant curvature-based Fourier shape descriptors
Journal of Visual Communication and Image Representation
Biometric feature extraction with biometric specific shape descriptors
International Journal of Biometrics
Shape-based image retrieval using pair-wise candidate co-ranking
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Shape is one of the fundamental visual features in the Content-based Image Retrieval (CBIR) paradigm. Numerous shape descriptors have been proposed in the literature. These can be broadly categorized as region-based and contour-based descriptors. Contourbased shape descriptors make use of only the boundary information, ignoring the shape interior content. Therefore, these descriptors cannot represent shapes for which the complete boundary information is not available. On the other hand, region-based descriptors exploit both boundary and internal pixels, and therefore are applicable to generic shapes. Among the region-based descriptors, moments have been very popular since they were first introduced in the 60's. In this paper we study and compare three moment-based descriptors: Invariant moments, Zernike moments, and radial Chebyshev moments. Experiments on the MPEG-7 shape databases show that radial Chebyshev moments achieve the highest retrieval performance.