Bayesian optimization of the scale saliency filter
Image and Vision Computing
Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
View Invariant Human Action Recognition Based on Factorization and HMMs
IEICE - Transactions on Information and Systems
Descriptive temporal template features for visual motion recognition
Pattern Recognition Letters
Behavior and properties of spatio-temporal local features under visual transformations
Proceedings of the international conference on Multimedia
Systematic evaluation of spatio-temporal features on comparative video challenges
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
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This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We derive a suitable distance measure between the representations, which is based on the Chamfer distance, and we optimize this measure with respect to a number of temporal and scaling parameters. In this way we achieve invariance against scaling, while at the same time, we eliminate the temporal differences between the representations. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed spatiotemporal salient point representation. The basis of these kernels is the optimized Chamfer distance of the previous step. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises.