Classification performance of violence content by deep neural network with monarch butterfly optimization

Abstract

Violence is self-sufficient, it is perplexing due to visibility of content dissimilarities among the positive instances that been displayed on media. Besides, the ever-increasing demand on internet, with various types of videos and genres, causes difficulty for a proper search of these videos to ensure the contents is humongous. It involves in aiding users to choose movies or web videos suitable for audience, in terms of classifying violence content. Nevertheless, this is a cumbersome job since the definition of violence is broad and subjective. Detecting such nuances from videos becomes technical without a human's supervision that can lead to conceptual problem. Generally, violence classification is performed based on text, audio, and visual features; to be precise, it is more relevant to use of audio and visual base. However, from this perspective, deep neural network is the current build-up in machine learning approach to solve classification problems. In this research, audio and visual features are learned by the deep neural network for more specific violence content classification. This study has explored the implementation of deep neural network with monarch butterfly optimization (DNNMBO) to effectively perform the classification of the violence content in web videos. Hence, the experiments are conducted using YouTube videos from VSD2014 dataset that are publicly available by Technicolor group. The results are compared with similar modified approaches such as DNNPSO and the original DNN. The outcome has shown 94% of violence classification rate by DNNMBO.