International Journal of Knowledge Engineering and Soft Data Paradigms
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Modeling music as a dynamic texture
IEEE Transactions on Audio, Speech, and Language Processing
Compressive acquisition of dynamic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Maximum margin distance learning for dynamic texture recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
A unified approach to segmentation and categorization of dynamic textures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Sustained observability for salient motion detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Shift-Invariant dynamic texture recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Large margin mixture of AR models for time series classification
Applied Soft Computing
Action recognition using linear dynamic systems
Pattern Recognition
Model-based kernel for efficient time series analysis
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Visual Communication and Image Representation
Car detection in sequences of images of urban environments using mixture of deformable part models
Pattern Recognition Letters
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We present a framework for the classification of visual processes that are best modeled with spatio-temporal auto-regressive models. The new framework combines the modeling power of a family of models known as dynamic textures and the generalization guarantees, for classification, of the support vector machine classifier. This combination is achieved by the derivation of a new probabilistic kernel based on the Kullback-Leibler divergence (KL) between Gauss-Markov processes. In particular, we derive the KL-kernel for dynamic textures in both 1) the image space, which describes both the motion and appearance components of the spatio-temporal process, and 2) the hidden state space, which describes the temporal component alone. Together, the two kernels cover a large variety of video classification problems, including the cases where classes can differ in both appearance and motion and the cases where appearance is similar for all classes and only motion is discriminant. Experimental evaluation on two databases shows that the new classifier achieves superior performance over existing solutions.