Abstract
The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance.
Highlights
Research on content-based visual information retrieval started in the 1990s
support vector machines (SVMs) were originally designed for two-class classification problems
Multi-class (M = 6) classification tasks were achieved using a one-against-the-rest approach, where an SVM was constructed for each class by discriminating that class against the remaining (M−1) classes
Summary
Research on content-based visual information retrieval started in the 1990s. Earlier retrieval systems concentrated on image data based on visual content, such as color, texture, and shape [1]. On the other hand, analyzing video content, which fully considers video temporality, has been an active research area for the past several years and is likely to attract even more attention in years to come [1]. Due to the decreasing cost of storage devices, higher transmission rates, and improved compression techniques, digital video is available in an ever-increasing rate. All of these popularized the use of video data for retrieval, browsing, and searching. Due to its vast volume, effective classification techniques are required for efficient retrieval, browsing, and searching of video data
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