An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction

Abstract

INTRODUCTION: As an essential part of life, the use of the Internet has increased exponentially. This rising Internet bandwidth speed has made video data transmission a more popular and modern form of information exchange. For classification of video date files there is a requirement of human efforts. Also for reducing the rate of clutter in video data on Internet, a suitable automatic video classification method is required.

OBJECTIVES: In this work, we tried to find a successful model for video classification.

METHODS: To make a successful model we use different schemes of visual and audio data analysis. On the other hand we choose some music, traffic and sports videos for different analysis. The model is based on Hidden Markov model (HMM) and Artificial neural network (ANN) classifiers. In order to gather the final results, we developed an “enhanced ANN-HMM based” model.

RESULTS: Our approach attained an average of 90% success rate among all three classification classes.

CONCLUSION: In aim of this work is to categorize and caption the videos automatically. Here we proposed an enhanced HMM-ANN based classification of video recordings with the aid of audio visual feature extraction.