Double constrained bag of words for human action recognition

Various improved methods based on the strategy of bag of words (BoW) are widely used to solve the problem of human action recognition. However, the spatial relationship between features is measured and utilized by these methods in a relatively single way. It limits the recognition performance of these methods. To solve this problem, double constrained bag of words (DC-BoW) is proposed to utilize the spatial distribution information between features belonging to three levels, which include descriptor-level, presentation-level and hidden layer features. Aiming at the problem that most coding methods only rely on Euclidean distance to constrain the relationship between descriptor-level features, the constraints of the difference in length and cosine of angle between visual word and local feature are designed to construct the loss function to obtain the length and angle constrained linear coding (LACLC) method. In order to improve the recognizability of the representation-level features, the spatial distribution between the encoded features around each cluster center is considered. Hierarchical weighting and LACLC are jointly applied to the distribution to construct aggregated word group feature (AWGF). At the same time, the constraint form of correntropy is changed according to the principle of constructing constraints in LACLC. The hidden layer features are combined with new constraint forms to construct double constrained extreme learning machine (DC-ELM), which improves the classification performance of the network while avoiding iterative training of correntropy weight. In order to verify the feasibility of DC-BoW, experiments are conducted on KTH, Olympic Sports, UCF11, Hollywood2 and UCF101 datasets. Experimental results show that the proposed DC-BoW can further utilize the spatial distribution information between features to obtain excellent recognition accuracy compared with other improved methods based on BoW.

Automated summary

The DC-BoW method improves recognition accuracy by effectively utilizing spatial distribution information between features and introducing double constraints.