Parallel two-class 3D-CNN classifiers for video classification
Jing Li · 2017
The required amount of computation and training data for training 3D-CNN, especially for complex classification tasks with videos, hinders the wide application of 3D-CNN. In this paper, inspired by the exclusion method in human's judgement, 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 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, which shows the potentiality of the proposed parallel two-class 3D-CNN model in video classification.