Unsupervised Video Summarization with Independently Recurrent Neural Networks

Abstract

Video summarization, which is one of the research topics that has gained significant acceleration over the past few years, is producing shorter and more concise videos that can represent the content of large videos as diversely as possible. It is observed that the hyperbolic tangent and the sigmoid action function used in long short-term memory (LSTM) and gated recurrent unit (GRU) models which are used in the recent studies on video summarization may cause gradient decay over layers. Besides, entanglement of neurons on recurrent neural network (RNN) can make it troublesome to interpret and develop these networks. In order to solve these issues, in this study, a method that uses deep reinforcement learning together with independently recurrent neural networks (IndRNN) is proposed for unsupervised video summarization problem. In this way, the model can be trained with more steps and has more layers without having any problem related to gradients. Based on the experiments conducted on two benchmark datasets, it is observed that compared to the state-of-the-art techniques on video summarization, better results are obtained.