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Image classification using L-GEM based RBFNN with local feature keypoints and MPEG-7 descriptors

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6 Author(s)
Qian-Cheng Wang ; Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China ; Daniel S. Yeung ; Wing W. Y. Ng ; Cheng-Hu Lin
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Image with MPEG-7 descriptors as features may loss local details. In this work, we combine MPEG-7 descriptors with local feature key points to cover both global and local image characteristics. Images are classified by a Radial Basis Function Neural Network (RBFNN) trained via a minimization of Localized Generalization Error Model (L-GEM). In this paper, we extract local feature key points by the Scale Invariant Feature Transform (SIFT). Four color and three texture MPEG-7 descriptors are extracted. Experimental results show that the introduction of local feature key points effectively improves the testing accuracy of image classification.

Published in:

2009 International Conference on Machine Learning and Cybernetics  (Volume:6 )

Date of Conference:

12-15 July 2009