Classification Performance of Violence Content by Deep Neural Network with Monarch Butterfly Optimization

Ashikin Ali,Saima Anwar,Iwan Tri,Norhalina Senan

doi:10.14569/ijacsa.2019.0101268

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.

Highlights

Sensible content filtering of media is necessary, as the multimedia content is affecting the internet users significantly
From the conventional Deep neural network (DNN), researcher learned that untreated data can be a reason of inefficacy of performance
Implementation of monarch butterfly optimization (MBO) algorithm with DNN has contributed in the improvement of the accuracy as it is compatible to each other

Summary

Introduction

Sensible content filtering of media is necessary, as the multimedia content is affecting the internet users significantly. It is evident that numerous web-filtering systems are highly available on internet These approaches did not meet user needs to accurately classify violence content that is exposed on internet or social media. To resolve this drawback, artificial neural network will be a feasible solution and has been an efficient method to classify violence content [2],[3],[4]. The inspiring simple and robust nature of the algorithm with the ability to deal with the stirring between exploration and exploitation has given strength to the choice This has motivated this study to embed the MBO with DNN in order to improve the performance accuracy based on the accuracy, precision and recall. In this study, both the audio-visual features and all the attributes of the features have been used to feed into the proposed classifier

Methods

Results

Conclusion