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Query by Few Video Examples Using Rough Set Theory and Partially Supervised Learning

  • Kimiaki Shirahama
  • Yuta Matsuoka
  • Kuniaki Uehara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6725)

Abstract

In this paper, we develop a video retrieval method based on Query-By-Example (QBE) approach, where a user represents a query by providing example shots. QBE then retrieves shots similar to example shots in terms of color, edge, motion, etc. We consider QBE as effective because the query is represented by features in example shots without the ambiguity of semantic contents. In addition, QBE can perform retrieval for any queries as long as the user can provide example shots.

References

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    Fung, G., Yu, J., Ku, H., Yu, P.: Text Classification without Negative Examples Revisit. IEEE Trans. Knowl. Data Eng. 18(1), 6–20 (2006)CrossRefGoogle Scholar
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    Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric Bagging and Random Subspace for Support Vector Machines-based Relevance Feedback in Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1088–1099 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kimiaki Shirahama
    • 1
  • Yuta Matsuoka
    • 2
  • Kuniaki Uehara
    • 3
  1. 1.Graduate School of EconomicsKobe UniversityKobeJapan
  2. 2.Graduate School of EngineeringKobe UniversityKobeJapan
  3. 3.Graduate School of System InformaticsKobe UniversityKobeJapan

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