Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 37, Issue 10, Pages 2085-2098Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2015.2400461
Keywords
Data representation; latent subspace; image understanding; feature learning; structure preserving
Funding
- 973 Program [2014CB347600]
- National Natural Science Foundation of China [61402228, 61472422, 61332016, 61103059]
- Natural Science Fund for Distinguished Young Scholars of Jiangsu Province [BK2012033]
- National Laboratory of Pattern Recognition
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To uncover an appropriate latent subspace for data representation, in this paper we propose a novel Robust Structured Subspace Learning (RSSL) algorithm by integrating image understanding and feature learning into a joint learning framework. The learned subspace is adopted as an intermediate space to reduce the semantic gap between the low-level visual features and the high-level semantics. To guarantee the subspace to be compact and discriminative, the intrinsic geometric structure of data, and the local and global structural consistencies over labels are exploited simultaneously in the proposed algorithm. Besides, we adopt the l(2,1)-norm for the formulations of loss function and regularization respectively to make our algorithm robust to the outliers and noise. An efficient algorithm is designed to solve the proposed optimization problem. It is noted that the proposed framework is a general one which can leverage several well-known algorithms as special cases and elucidate their intrinsic relationships. To validate the effectiveness of the proposed method, extensive experiments are conducted on diversity datasets for different image understanding tasks, i.e., image tagging, clustering, and classification, and the more encouraging results are achieved compared with some state-of-the-art approaches.
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