JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 4    Issue : 7    Date : July 2009

A Novel Video Content Understanding Scheme Based on Feature Combination Strategy
Xinghao Jiang, Tanfeng Sun, Bin Chen, Rongjie Li, and Bing Feng
Page(s): 615-622
Full Text:
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Abstract
With the development of the multimedia technology, there are more and more video resources on
the Internet, which are difficult to automatically recognize, classify and index. So solve these
problems, we present a novel video content understanding scheme in this paper. This scheme is
based on the combination strategy of different video features. To represent these video features, we
use nine standard MPEG-7 descriptors, including color, texture, region and motion descriptors. We
extract and combine these descriptors together to represent the whole video character. After that, we
use an SVM as the classifier to train the model. The traditional 1-1 method of the SVM is modified by
a Second-Prediction Strategy to gain higher classification accuracy. Finally, the videos are classified
into five genres, including cartoons, commercial, music, news, and sports. We compare our
classification results with some of the results in the recent papers, and demonstrate the
effectiveness of our scheme.

Index Terms
content understanding, video content classification, MPEG-7 descriptors, second-prediction,
support vector machine