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Hierarchical discriminant manifold learning for dimensionality reduction and image classification

[+] Author Affiliations
Weihai Chen, Changchen Zhao, Kai Ding, Xingming Wu

Beihang University, School of Automation Science and Electrical Engineering, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China

Peter C. Y. Chen

National University of Singapore, Department of Mechanical Engineering, Faculty of Engineering, 21 Lower Kent Ridge Road, Singapore 119077, Singapore

J. Electron. Imaging. 24(5), 053015 (Sep 21, 2015). doi:10.1117/1.JEI.24.5.053015
History: Received April 28, 2015; Accepted August 12, 2015
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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.

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Citation

Weihai Chen ; Changchen Zhao ; Kai Ding ; Xingming Wu and Peter C. Y. Chen
"Hierarchical discriminant manifold learning for dimensionality reduction and image classification", J. Electron. Imaging. 24(5), 053015 (Sep 21, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.5.053015


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