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The Scientific World Journal
Volume 2014, Article ID 924090, 11 pages
Research Article

Multiview Discriminative Geometry Preserving Projection for Image Classification

School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China

Received 19 December 2013; Accepted 22 January 2014; Published 9 March 2014

Academic Editors: X. Meng, Z. Zhou, and X. Zhu

Copyright © 2014 Ziqiang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm.