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Proceedings Paper

CW-SSIM kernel based random forest for image classification
Author(s): Guangzhe Fan; Zhou Wang; Jiheng Wang
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Paper Abstract

Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.

Paper Details

Date Published: 4 August 2010
PDF: 8 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774425 (4 August 2010); doi: 10.1117/12.863528
Show Author Affiliations
Guangzhe Fan, Univ. of Waterloo (Canada)
Zhou Wang, Univ. of Waterloo (Canada)
Jiheng Wang, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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