@article {15065,
	title = {Class consistent k-means: Application to face and action recognition},
	journal = {Computer Vision and Image Understanding},
	volume = {116},
	year = {2012},
	month = {2012/06//},
	pages = {730 - 741},
	abstract = {A class-consistent k-means clustering algorithm (CCKM) and its hierarchical extension (Hierarchical CCKM) are presented for generating discriminative visual words for recognition problems. In addition to using the labels of training data themselves, we associate a class label with each cluster center to enforce discriminability in the resulting visual words. Our algorithms encourage data points from the same class to be assigned to the same visual word, and those from different classes to be assigned to different visual words. More specifically, we introduce a class consistency term in the clustering process which penalizes assignment of data points from different classes to the same cluster. The optimization process is efficient and bounded by the complexity of k-means clustering. A very efficient and discriminative tree classifier can be learned for various recognition tasks via the Hierarchical CCKM. The effectiveness of the proposed algorithms is validated on two public face datasets and four benchmark action datasets.},
	keywords = {Action recognition, Class consistent k-means, Discriminative tree classifier, face recognition, Supervised clustering},
	isbn = {1077-3142},
	doi = {10.1016/j.cviu.2012.02.004},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314212000367},
	author = {Zhuolin Jiang and Zhe Lin and Davis, Larry S.}
}
@article {11906,
	title = {Silhouette-based gesture and action recognition via modeling trajectories on Riemannian shape manifolds},
	journal = {Computer Vision and Image Understanding},
	volume = {115},
	year = {2011},
	month = {2011/03//},
	pages = {439 - 455},
	abstract = {This paper addresses the problem of recognizing human gestures from videos using models that are built from the Riemannian geometry of shape spaces. We represent a human gesture as a temporal sequence of human poses, each characterized by a contour of the associated human silhouette. The shape of a contour is viewed as a point on the shape space of closed curves and, hence, each gesture is characterized and modeled as a trajectory on this shape space. We propose two approaches for modeling these trajectories. In the first template-based approach, we use dynamic time warping (DTW) to align the different trajectories using elastic geodesic distances on the shape space. The gesture templates are then calculated by averaging the aligned trajectories. In the second approach, we use a graphical model approach similar to an exemplar-based hidden Markov model, where we cluster the gesture shapes on the shape space, and build non-parametric statistical models to capture the variations within each cluster. We model each gesture as a Markov model of transitions between these clusters. To evaluate the proposed approaches, an extensive set of experiments was performed using two different data sets representing gesture and action recognition applications. The proposed approaches not only are successfully able to represent the shape and dynamics of the different classes for recognition, but are also robust against some errors resulting from segmentation and background subtraction.},
	keywords = {Action recognition, Gesture recognition, Riemannian manifolds, Shape space, Silhouette-based approaches},
	isbn = {1077-3142},
	doi = {16/j.cviu.2010.10.006},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314210002377},
	author = {Abdelkader,Mohamed F. and Abd-Almageed, Wael and Srivastava,Anuj and Chellapa, Rama}
}
@conference {14227,
	title = {Learning shift-invariant sparse representation of actions},
	booktitle = {2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {2630 - 2637},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A central problem in the analysis of motion capture (MoCap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should facilitate the recognition, synthesis, and characterization of actions. We propose an unsupervised learning algorithm for automatically decomposing joint movements in human motion capture (MoCap) sequences into shift-invariant basis functions. Our formulation models the time series data of joint movements in actions as a sparse linear combination of short basis functions (snippets), which are executed (or {\textquotedblleft}activated{\textquotedblright}) at different positions in time. Given a set of MoCap sequences of different actions, our algorithm finds the decomposition of MoCap sequences in terms of basis functions and their activations in time. Using the tools of L1 minimization, the procedure alternately solves two large convex minimizations: Given the basis functions, a variant of Orthogonal Matching Pursuit solves for the activations, and given the activations, the Split Bregman Algorithm solves for the basis functions. Experiments demonstrate the power of the decomposition in a number of applications, including action recognition, retrieval, MoCap data compression, and as a tool for classification in the diagnosis of Parkinson (a motion disorder disease).},
	keywords = {action characterization, Action recognition, action retrieval, action synthesis, Character recognition, data compression, human motion capture, HUMANS, Image matching, Image motion analysis, image representation, Image sequences, Information retrieval, joint movements, large convex minimizations, learning (artificial intelligence), learning shift-invariant sparse representation, Matching pursuit algorithms, minimisation, Minimization methods, MoCap data compression, Motion analysis, motion capture analysis, motion disorder disease, motion sequences, orthogonal matching pursuit, Parkinson diagnosis, Parkinson{\textquoteright}s disease, Pursuit algorithms, shift-invariant basis functions, short basis functions, snippets, sparse linear combination, split Bregman algorithm, time series, time series data, Unsupervised learning, unsupervised learning algorithm},
	isbn = {978-1-4244-6984-0},
	doi = {10.1109/CVPR.2010.5539977},
	author = {Li,Yi and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Hui Ji}
}
