Title :
Robust Affine Invariant Region-Based Shape Descriptors: The ICA Zernike Moment Shape Descriptor and the Whitening Zernike Moment Shape Descriptor
Author :
Mei, Ye ; Androutsos, Dimitrios
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
In this letter, we proposed two new affine invariant region-based shape descriptors, the ICA Zernike moment shape descriptor (ICAZMSD) and the whitening Zernike moment shape descriptor (WZMSD). Either independent component analysis (ICA) or whitening, is first used to turn the original shape into a canonical form, in which the effects of scaling and skewing are eliminated. Next, the properties of the Zernike transform are used to further eliminate the effects of any possible rotation and reflection of the canonical shapes, in extracting the Zernike moments as the affine invariant region-based descriptors. Using the proposed ICAZMSD as shape feature, shape-based image retrieval experiments on a 4000 complex shape image database and on a 5600 simple shape image database, show retrieval rates of 99.80% and 92.25%, respectively. Using the proposed WZMSD as shape feature, the corresponding retrieval rates are 99.79% and 92.22%, respectively. The proposed WZMSD has almost equal performance to the proposed ICAZMSD, while having lower computational requirements.
Keywords :
image retrieval; independent component analysis; matrix algebra; visual databases; Zernike moment shape descriptor; affine invariant region-based shape descriptors; image database; independent component analysis; shape-based image retrieval; whitening Zernike moment shape descriptor; Affine invariant; Independent Component Analysis (ICA); Zernike moments; shape descriptor; whitening;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2009.2026119