Indian Classical Dance Classification on Manifold Using Jensen-Bregman LogDet Divergence

Due to occlusion, lighting condition, variation in clothing dance video classification is a challenging problem in computer vision domain. In this paper we present a local spatiotemporal feature model on manifold for Indian Classical Dance (ICD) classification. We represent features at each space-time interest point as a covariance matrix by fusing different order spatial and temporal derivatives. Each video clip is then represented in bag-of-words framework on manifold using Jensen-Bregman LogDet Divergence. Classification is done by popular non-linear SVM with ?2-kernel. We evaluate our system on a ICD dataset created from YouTube and get 69.39% accuracy which is better than that of the state-of-the-art human activity classification algorithms. We have also tested our algorithms on human activity benchmark datasets like KTH, and UCF50 and get promising results compared to the state-of-the-art methods.