Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume 40, Issue 6, Pages 1438-1446Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2009.2039566
Keywords
Dimensionality reduction; multiple views; spectral embedding
Categories
Funding
- National Basic Research Program of China (973 Program) [2007CB311100]
- National High-Technology Research and Development Program of China (863 Program) [2007AA01Z416]
- National Natural Science Foundation of China [60873165, 60802028, 60902090]
- Beijing New Star Project on Science and Technology [2007B071]
- Beijing Municipal Education Commission
- Nanyang Technological University Nanyang SUG [M58020010]
- Microsoft Operations PTE LTD-NTU Joint RD [M48020065]
- K. C. Wong Education Foundation
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In computer vision and multimedia search, it is common to use multiple features from different views to represent an object. For example, to well characterize a natural scene image, it is essential to find a set of visual features to represent its color, texture, and shape information and encode each feature into a vector. Therefore, we have a set of vectors in different spaces to represent the image. Conventional spectral-embedding algorithms cannot deal with such datum directly, so we have to concatenate these vectors together as a new vector. This concatenation is not physically meaningful because each feature has a specific statistical property. Therefore, we develop a new spectral-embedding algorithm, namely, multiview spectral embedding (MSE), which can encode different features in different ways, to achieve a physically meaningful embedding. In particular, MSE finds a low-dimensional embedding wherein the distribution of each view is sufficiently smooth, and MSE explores the complementary property of different views. Because there is no closed-form solution for MSE, we derive an alternating optimization-based iterative algorithm to obtain the low-dimensional embedding. Empirical evaluations based on the applications of image retrieval, video annotation, and document clustering demonstrate the effectiveness of the proposed approach.
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