Audio-video based Segmentation and Classification using SVM and AANN

Abstract

In this paper, we propose a method for combining audio and video for segmentation and classification. The objective of segmentation is to detect the category change point such news to advertisement. The classification system classify the audio-video data into one of the predefined categories such as news, advertisement, sports, serial and movies. Mel frequency cepstral coefficients(MFCC) are used as acoustic features and color histogram is used as visual features for segmentation and classification. Support vector machine(SVM) and autoassociative neural network(AANN) models are used for segmentation and classification. The evidence from audio and video are combined using weighted sum rule for both segmentation and classifications.