Paper Info
Title
Category Specific Dictionary Learning for Attribute Specific Feature Selection.
Abstract
Attributes, as mid-level features, have demonstrated great potential in visual recognition tasks due to their excellent propagation capability through different categories. However, existing attribute learning methods are prone to learning the correlated attributes. To discover the genuine attribute specific features, many feature selection methods have been proposed. However, these feature selection methods are implemented at the level of raw features that might be very noisy, and these methods usually fail to consider the structural information in the feature space. To address this issue, in this paper, we propose a label constrained dictionary learning approach combined with a multilayer filter. The feature selection is implemented at dictionary level, which can better preserve the structural information. The label constrained dictionary learning suppresses the intra-class noise by encouraging the sparse representations of intra-class samples to lie close to their center. A multilayer filter is developed to discover the representative and robust attribute specific bases. The attribute specific bases are only shared among the positive samples or the negative samples. The experiments on the challenging Animals with Attributes data set and the SUN attribute data set demonstrate the effectiveness of our proposed method.
Year
DOI
Venue
2016
10.1109/TIP.2016.2523340
IEEE Trans. Image Processing
Keywords
Field
DocType
Semantics,Nonhomogeneous media,Dictionaries,Image color analysis,Visualization,Vocabulary
Attribute learning,Feature vector,Dictionary learning,Feature selection,Pattern recognition,Computer science,Visual recognition,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
25
3
1057-7149
Citations 
PageRank 
References 
25
0.68
52
Authors
4
Name
Order
Citations
PageRank
Wei Wang113114.16
Yan Yan269131.13
Stefan Winkler321621.60
Nicu Sebe47013403.03