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Journal of Electronic Imaging

Hierarchical discriminant manifold learning for dimensionality reduction and image classification
Author(s): Weihai Chen; Changchen Zhao; Kai Ding; Xingming Wu; Peter C. Y. Chen
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Paper Abstract

In the field of image classification, it has been a trend that in order to deliver a reliable classification performance, the feature extraction model becomes increasingly more complicated, leading to a high dimensionality of image representations. This, in turn, demands greater computation resources for image classification. Thus, it is desirable to apply dimensionality reduction (DR) methods for image classification. It is necessary to apply DR methods to relieve the computational burden as well as to improve the classification accuracy. However, traditional DR methods are not compatible with modern feature extraction methods. A framework that combines manifold learning based DR and feature extraction in a deeper way for image classification is proposed. A multiscale cell representation is extracted from the spatial pyramid to satisfy the locality constraints for a manifold learning method. A spectral weighted mean filtering is proposed to eliminate noise in the feature space. A hierarchical discriminant manifold learning is proposed which incorporates both category label and image scale information to guide the DR process. Finally, the image representation is generated by concatenating dimensionality reduced cell representations from the same image. Extensive experiments are conducted to test the proposed algorithm on both scene and object recognition datasets in comparison with several well-established and state-of-the-art methods with respect to classification precision and computational time. The results verify the effectiveness of incorporating manifold learning in the feature extraction procedure and imply that the multiscale cell representations may be distributed on a manifold.

Paper Details

Date Published: 21 September 2015
PDF: 9 pages
J. Electron. Imag. 24(5) 053015 doi: 10.1117/1.JEI.24.5.053015
Published in: Journal of Electronic Imaging Volume 24, Issue 5
Show Author Affiliations
Weihai Chen, BeiHang Univ. (China)
Changchen Zhao, BeiHang Univ. (China)
Kai Ding, BeiHang Univ. (China)
Xingming Wu, BeiHang Univ. (China)
Peter C. Y. Chen, National Univ. of Singapore (Singapore)

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