Correlation preserved dictionary learning for sparse representation
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Self-paced dictionary learning for image classification
Proceedings of the 20th ACM international conference on Multimedia
Discriminative ICA model with reconstruction constraint for image classification
Proceedings of the 20th ACM international conference on Multimedia
Sparse embedding: a framework for sparsity promoting dimensionality reduction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Sparse coding based visual tracking: Review and experimental comparison
Pattern 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
Context enhanced graphical model for object localization in medical images
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Learning a context aware dictionary for sparse representation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Manifold based sparse representation for facial understanding in natural images
Image and Vision Computing
Learning group-based dictionaries for discriminative image representation
Pattern Recognition
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
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A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistent constraint called 'discriminative sparse-code error' and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single over-complete dictionary and an optimal linear classifier jointly. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse coding techniques for face and object category recognition under the same learning conditions.