ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
A dictionary learning approach for classification: separating the particularity and the commonality
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Characterization of task-free/task-performance brain states
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Human age estimation using ranking SVM
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
An improved fisher discriminant dictionary learning for video object tracking
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Discriminative dictionary learning with pairwise constraints
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Design of non-linear discriminative dictionaries for image classification
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
The role of dictionary learning on sparse representation-based classification
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Integration of multi-feature fusion and dictionary learning for face recognition
Image and Vision Computing
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
Robust face recognition via occlusion dictionary learning
Pattern Recognition
An adaptive regularization method for sparse representation
Integrated Computer-Aided Engineering
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Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.