Automatic sports genre categorization and view-type classification over large-scale dataset

ABSTRACT

This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications.

REFERENCES

• 1: J. Huang, et al., Joint scene classification and segmentation based on hidden Markov model. IEEE Transactions on Multimedia, Volume 7, Issue 3, pp. 538 -- 550, 2005.
• 2: H. Bay, et al., Surf: Speeded up robust features. Lecture Notes in Computer Science, 3951:404, 2006.
• 3: D. Brezeale and D. Cook. Automatic video classification: A survey of the literature. IEEE Transactions on Systems, Man, and Cybernetics, 38(3):416--430, 2008.
• 4: C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001.
• 5: L. Duan, et al., A unified framework for semantic shot classification in sports video. IEEE Transactions on Multimedia, 7(6), 2005.
• 6: A. Ekin, et al., Automatic Soccer Video Analysis and Summarization. IEEE Transactions on Image Processing, 12(7), 2003.
• 7: X. Gibert, et al., Sports video classification using HMMs. IEEE International Conference on Multimedia and Expo, pp. 345--348, 2003.
• 8: E. Jaser, et al., Hierarchical decision making scheme for sports video categorisation with temporal post-processing. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 908--913, 2004.
• 9: K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10):1615--1630, 2005.
• 10: S. Takagi, et al., Sports video categorizing method using camera motion parameters. IEEE International Conference on Multimedia and Expo, pp. 461--464, 2003.
• 11: B. Truong and C. Dorai. Automatic genre identification for content--based video categorization. International Conference on Pattern Recognition, pp. 230--233, 2000.
• 12: J. Wang, et al., Automatic sports video genre classification using pseudo-2d-hmm. International Conference on Pattern Recognition, pp. 778--781, 2006.
• 13: X. Yuan, et al., Automatic video genre categorization using hierarchical SVM. IEEE International conference on Image Processing, pp. 2905--2908, 2006.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
codebook, genre categorization, scene classification