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Proceedings Paper

Color scene classification by Zernike moment invariants
Author(s): Yongjun Zhou; Mehmet Celenk
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Paper Abstract

Hu and Zernike moments have been widely used in many shape recognition and object classification tasks. The Hu moments are the projections of an image I(x,y) on the basis functions formed by the monomials xPyq of non-orthogonal nature. Zernike moments are computed based on a set of orthogonal polynomials over the interior of the unit circle x2+y2<EQ1 in the image center using from the image pixels mapped into the unit circle and ignoring pixels falling outside the unit circle. Although comparative studies show that the Zernike polynomials have better noise performance in the context of character recognition and capable of coping both geometry as well as illumination invariance in the context of multispectral texture classification, there is no study conducted focusing on the application of Zernike moments to color image databases. In this paper, we study the performance of Zernike moments against the moments of Hu, in the context of RTSS (Rotation, translation, scaling)-invariant color scene classification, and determine which method behaves better in noisy visual information system. The method described here relies on the principal component analysis (PCA) and the discrete Karhunen-Loeve (KL) transformation for converting a vector- valued color image into a gray-level scale representation. For a selected set of images involving different models of vehicles, we demonstrate experimentally that the first two Zernike moments are highly immune to the sensory noise and also invariant to changes in the position, orientation, and size. They provide better classification power than the Hu moments in the presence of Gaussian noise.

Paper Details

Date Published: 28 August 2001
PDF: 10 pages
Proc. SPIE 4388, Visual Information Processing X, (28 August 2001); doi: 10.1117/12.438274
Show Author Affiliations
Yongjun Zhou, Ohio Univ. (United States)
Mehmet Celenk, Ohio Univ. (United States)

Published in SPIE Proceedings Vol. 4388:
Visual Information Processing X
Stephen K. Park; Zia-ur Rahman; Robert A. Schowengerdt, Editor(s)

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