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Automatic content-based retrieval and semantic classification of video content

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Abstract

The problem of video classification can be viewed as discovering the signature patterns in the elemental features of a video class. In order to solve this problem, a large and diverse set of video features is proposed in this paper. The contributions of the paper further lie in dealing with high-dimensionality induced by the feature space and in presenting an algorithm based on two-phase grid searching for automatic parameter selection for support vector machine (SVM). The framework thus is directed to bridge the gap between low-level features and semantic video classes. The experimental results and comparison with state-of-the-art learning tools on more than 5000 video segments show the effectiveness of our approach.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee

    Ankush Mittal & Sumit Gupta

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  1. Ankush Mittal
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  2. Sumit Gupta
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Correspondence to Ankush Mittal.

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Mittal, A., Gupta, S. Automatic content-based retrieval and semantic classification of video content. Int J Digit Libr 6, 30–38 (2006). https://doi.org/10.1007/s00799-005-0119-y

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