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Audio-Video based Classification using SVM and AANN

by K. Subashini, S. Palanivel, V. Ramalingam
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 6
Year of Publication: 2012
Authors: K. Subashini, S. Palanivel, V. Ramalingam
10.5120/6269-8425

K. Subashini, S. Palanivel, V. Ramalingam . Audio-Video based Classification using SVM and AANN. International Journal of Computer Applications. 44, 6 ( April 2012), 33-39. DOI=10.5120/6269-8425

@article{ 10.5120/6269-8425,
author = { K. Subashini, S. Palanivel, V. Ramalingam },
title = { Audio-Video based Classification using SVM and AANN },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 6 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number6/6269-8425/ },
doi = { 10.5120/6269-8425 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:52.064369+05:30
%A K. Subashini
%A S. Palanivel
%A V. Ramalingam
%T Audio-Video based Classification using SVM and AANN
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 6
%P 33-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method to classify audio-video data into one of five classes: advertisement, cartoon, news, movie and songs. Automatic audio-video classification is very useful to audio-video indexing, content based audio-video retrieval. Mel frequency cepstral coefficients are used to characterize the audio data. The color histogram features extracted from the images in the video clips are used as visual features. The experiments on different genres illustrate the results of classification are significant and effective. Experimental results of audio classification and video classification are combined using weighted sum rule for audio-video based classification. The method SVM and AANN classifies the audio-video clips with an accuracy of 95. 54%. , and 92. 94%.

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Index Terms

Computer Science
Information Sciences

Keywords

Mel Frequency Cepstral Coefficients Color Histogram Auto Associative Neural Network Audio Segmentation Video Segmentation Audio Classification Video Classification Audio-video Classification Weighted Sum Rule

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