Multiple feature fusion by subspace learning

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 ...

• Type: Conference
• Year: 2008
• Where: CIVR
• Authors: Yun Fu, Liangliang Cao, Guodong Guo, Thomas S. Huang
• Keywords: Canonical Correlations, CIVR 2008, Dimensionality Reduction, Feature Fusion, Image Analysis