A Hybrid Model used for Audio Video Classification
Abstract
In this paper we present a system to categorize audio-video files into one of five modules: news, movie, advertisement, cartoon,
and songs. Spontaneous audio-video classification is very useful to audio-video indexing, content based audio-video retrieval.
MFCCs are used to distinguish the audio data. The color histogram features mined from the images in the video clips are used as
graphic features. SVM (Support Vector Machine) is used for audio and video segmentation. ANN (Artificial Neural Network) is
used for audio and video classification. The trials on different fields illustrate the results of segmentation and classifications are
significant and effective. Trial results of audio and video segmentation or classification results are combined using weighted sum
(WS) rule for audio-video based classification. Combining the features of SVM and ANN techniques, system classifies the audiovideo
clips with effective and efficient manner to obtain accurate result.
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