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The Bag of Words (BoW) approach has been widely used for human action recognition in recent state-of-the-art methods. In this paper, we introduce what we call a Bag of Expression (BoE) framework, based on the bag of words method, for recognizing human action in simple and realistic scenarios. The proposed approach includes space time neighborhood information in addition to visual words. The main focus is to enhance the existing strengths of the BoW approach like view independence, scale invariance and occlusion handling. BOE includes independent pairs of neighbors for building expressions, therefore it is tolerant to occlusion and capable of handling view independence up to some extent in realistic scenarios. Our main contribution includes learning a class specific visual words extraction approach for establishing a relationship between these extracted visual words in both space and time dimension. Finally, we have carried out a set of experiments to optimize different parameters and compare its performance with recent state-of-the-art-methods. Our approach outperforms existing Bag of Words based approaches, when evaluated using the same performance evaluation methods. We tested our approach on four publicly available datasets for human action recognition i.e. UCF-Sports, KTH, UCF11 and UCF50 and achieve significant results i.e. 97.3%, 99.5%, 96.7% and 93.42% respectively in terms of average accuracy.
human action recognition; bag of words; bag of visual words