CW-SSIM kernel based random forest for image classification

Guangzhe Fan; Zhou Wang; Jiheng Wang

doi:10.1117/12.863528

4 August 2010 CW-SSIM kernel based random forest for image classification

Guangzhe Fan, Zhou Wang, Jiheng Wang

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 774425 (2010) https://doi.org/10.1117/12.863528
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

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.

Citation Download Citation

Guangzhe Fan, Zhou Wang, and Jiheng Wang "CW-SSIM kernel based random forest for image classification", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 774425 (4 August 2010); https://doi.org/10.1117/12.863528

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available