Unified discriminating feature analysis for visual category recognition
- Publisher:
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Publication Type:
- Journal Article
- Citation:
- Journal of Visual Communication and Image Representation, 2016, 40, pp. 772-778
- Issue Date:
- 2016-10-01
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1-s2.0-S1047320316301250-main.pdf | Published version | 352.47 kB |
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Visual category recognition (VCR) is one of the most important tasks in image and video indexing. To deal with high dimension image/video data, feature analysis algorithms have been widely used for visual category recognition. In this paper, to enhance the flexibility regarding the exploitation of labeled or unlabeled data, we propose a unified feature analysis framework that can be applied to both supervised and semi-supervised scenarios. Furthermore, by revealing intrinsic relationships of traditional feature analysis methods, our framework not only integrates traditional methods, but also introduces an ℓ2,1-norm regularization term for sparse learning. Extensive experiments report that the proposed method obtains advantageous performance in comparison with other state-of-the-art supervised and semi-supervised feature selection algorithms.
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