@conference {15066,
	title = {Learning a discriminative dictionary for sparse coding via label consistent K-SVD},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {1697 - 1704},
	abstract = {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 {\textquoteleft}discriminative sparse-code error{\textquoteright} 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.},
	keywords = {classification error, Dictionaries, dictionary learning process, discriminative sparse code error, face recognition, image classification, Image coding, K-SVD, label consistent, learning (artificial intelligence), object category recognition, Object recognition, optimal linear classifier, reconstruction error, singular value decomposition, Training data},
	doi = {10.1109/CVPR.2011.5995354},
	author = {Zhuolin Jiang and Zhe Lin and Davis, Larry S.}
}
@conference {12436,
	title = {Sparse dictionary-based representation and recognition of action attributes},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {707 - 714},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and class distribution for each dictionary item. We propose a Gaussian Process (GP) model for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with a compact support in GP to realize a very efficient dictionary learning process. Hence we can describe an action video by a set of compact and discriminative action attributes. More importantly, we can recognize modeled action categories in a sparse feature space, which can be generalized to unseen and unmodeled action categories. Experimental results demonstrate the effectiveness of our approach in action recognition applications.},
	keywords = {action attributes, appearance information, class distribution, Dictionaries, dictionary learning process, Encoding, Entropy, Gaussian process model, Gaussian processes, Histograms, HUMANS, Image coding, image representation, information maximization, learning (artificial intelligence), modeled action categories, Mutual information, Object recognition, probabilistic logic, sparse coding property, sparse dictionary-based recognition, sparse dictionary-based representation, sparse feature space, unmodeled action categories},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126307},
	author = {Qiang Qiu and Zhuolin Jiang and Chellapa, Rama}
}
