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Automatic Caption Detection in Video Frames Based on Support Vector Machine

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Book cover Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

Video captions can be used to index large video archives in digital libraries. In this paper, an algorithm for detecting captions in video frames using support vector machine (SVM) is proposed. First, the input video frame is divided into square sub-blocks and a trained SVM is used to identify whether each sub-block is a caption block or not. Second, horizontal projection and vertical projection are performed to locate the candidate caption regions. Finally, false alarms are reduced by caption region verification. Experimental results show that the algorithm has a low missed rate and false alarm rate.

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Author information

Authors and Affiliations

  1. School of Computer Science, South China University of Technology, Guangdong, Guangzhou, 510640, China

    Jianfeng Xu & Shaofa Li

Authors
  1. Jianfeng Xu
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  2. Shaofa Li
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Xu, J., Li, S. (2005). Automatic Caption Detection in Video Frames Based on Support Vector Machine. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_41

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  • DOI: https://doi.org/10.1007/11427445_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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