Research Article
An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction
@ARTICLE{10.4108/eai.31-3-2021.169172, author={Pooja Mehta and Sahil Kaswan and Jaspreet Kaur}, title={An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction}, journal={EAI Endorsed Transactions on Creative Technologies}, volume={8}, number={28}, publisher={EAI}, journal_a={CT}, year={2021}, month={3}, keywords={Hidden Markov model, Artificial neural network, Zero Forcing Equaliser, Mel- frequency cepstral coefficient, Neural Network}, doi={10.4108/eai.31-3-2021.169172} }- Pooja Mehta
Sahil Kaswan
Jaspreet Kaur
Year: 2021
An Enhanced ANN-HMM based classification of video recordings with the aid of audio-visual feature extraction
CT
EAI
DOI: 10.4108/eai.31-3-2021.169172
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.
Copyright © 2021 Pooja Mehta et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
