NDLI: Multiple feature fusion by subspace learning

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Multiple feature fusion by subspace learning

Content Provider	ACM Digital Library
Author	Guo, Guodong Fu, Yun Huang, Thomas S. Cao, Liangliang
Abstract	Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion techniques simply concatenate a pair of different features or use canonical correlation analysis based methods for joint dimensionality reduction in the feature space. However, how to fuse multiple features in a generalized way is still an open problem. In this paper, we reformulate the multiple feature fusion as a general subspace learning problem. The objective of the framework is to find a general linear subspace in which the cumulative pairwise canonical correlation between every pair of feature sets is maximized after the dimension normalization and subspace projection. The learned subspace couples dimensionality reduction and feature fusion together, which can be applied to both unsupervised and supervised learning cases. In the supervised case, the pairwise canonical correlations of feature sets within the same classes are also counted in the objective function for maximization. To better model the high-order feature structure and overcome the computational difficulty, the features extracted from the same pattern source are represented by a single 2D tensor. The tensor-based dimensionality reduction methods are used to further extract low-dimensional discriminative features from the fused feature ensemble. Extensive experiments on visual data classification demonstrate the effectiveness and robustness of the proposed methods.
Starting Page	127
Ending Page	134
Page Count	8
File Format	PDF
ISBN	9781605580708
DOI	10.1145/1386352.1386373
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Tensor Face recognition Subspace learning Feature fusion Canonical correlation Image set
Content Type	Text
Resource Type	Article

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