Effect of wavelet and hybrid classification on action recognition
Proceedings - International Conference on Image Processing, ICIP
Action recognition, Data Compression, Hybrid classification, Motion-based features
Any action dataset may contain similar classes such as running, walking and jogging. Therefore, equivalent probabilities may be provided for different classes upon action classification. In this case, the classifier cannot indubitably assign a class to a given sample. To address this problem, we propose a new hybrid classifier to automatically compress the features and classify them using SVM with polynomial or sigmoid kernels. Furthermore, we hypothesize that motion saliency detection can strength the power of motion feature extraction in the bag of visual words framework (BoVW). To this end, we evaluate the effect of 3D-discrete wavelet transform (3D-DWT), as the preprocessing step, on motion feature extraction. The experimental results show that the proposed framework achieves promising results on KTH, Weizmann, and URADL datasets, and outperforms recent state-of-the-art approaches.
Mohammadi, Eman; Wu, Q. M.Jonathan; Yang, Yimin; and Saif, Mehrdad. (2017). Effect of wavelet and hybrid classification on action recognition. Proceedings - International Conference on Image Processing, ICIP, 2017-September, 1787-1791.