A novel keyframe extraction method for video classification using deep neural networks

This paper was funded by (1) the Turkish Ministry of National Education, (2) the Spanish Ministry of Science, Innovation, and Universities under Grant PGC2018-098813-B-C31, and (3) ERDF fund.Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy.Ministry of National Education - TurkeySpanish Ministry of Science, Innovation, and Universities PGC2018-098813-B-C31ERDF fun