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The emergence of depth imaging technologies like the Microsoft Kinect hasrenewed interest in computational methods for gesture classification based onvideos. For several years now, researchers have used the Bag-of-Features (BoF)as a primary method for generation of feature vectors from video data forrecognition of gestures. However, the BoF method is a coarse representation ofthe information in a video, which often leads to poor similarity measuresbetween videos. Besides, when features extracted from different spatio-temporallocations in the video are pooled to create histogram vectors in the BoFmethod, there is an intrinsic loss of their original locations in space andtime. In this paper, we propose a new Multiresolution Match Kernel (MMK) forvideo classification, which can be considered as a generalization of the BoFmethod. We apply this procedure to hand gesture classification based on RGB-Dvideos of the American Sign Language(ASL) hand gestures and our results showpromise and usefulness of this new method.

Multiresolution Match Kernels for Gesture Video Classification