Two-class 3D-CNN classifiers combination for video copy detection

3D-CNN is the latest CNN model used for video classification. However, the required amount of computation and training data for training 3D-CNN, especially for complex classification tasks with large video data, hinders the wide application of 3D-CNN. In this paper, inspired by the exclusion method in human’s judgment, a parallel 3D-CNN architecture is proposed to decompose the multi-class classification task using one 3D-CNN into the combination of multiple two-class classification tasks. 3D-CNN is used as a two-class classifier for each of the two-class classification tasks, and the difficulty and the data requirement on training such a 3D-CNN is reduced greatly comparing with the 3D-CNN for multi-class classification. In addition, the combination of two-class classifiers provides the ability of recognizing unknown class to the proposed 3D-CNN model. The feasibility of this proposed 3D-CNN model is verified via its application on video copy detection on the CC_WEB_VIDEO dataset. The experimental results show the potentiality of the proposed parallel two-class 3D-CNN model in video classification.