An online-optimized incremental learning framework for video semantic classification

ABSTRACT

This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic classification framework, termed OOIL (for Online-Optimized Incremental Learning), in which two sets of optimized classification models, local and global, are online trained by sufficiently exploiting both local and global statistic characteristics of videos. The global models are pre-trained on a relatively small set of pre-labeled samples. And the local models are optimized for the under-test video or video segment by checking a small portion of unlabeled samples in this video, while they are also applied to incrementally update the global models. Experiments have illustrated promising results on simulated data as well as real sports videos.

REFERENCES

• 1: P. Xu, et al, Algorithms and Systems for Segmentation and Structure Analysis in Soccer Video, ICME2001, pp 22--25.
• 2: L. Xie, et al, Structure Analysis of Soccer Video with Hidden Markov Models, ICASSP 2002, Vol 4, pp 13--17.
• 3: Chua Hock Chuan, Automatic Parsing of TV Soccer Programs, Proceedings of the International Conference on Multimedia Computing and Systems, p.167, May 15-18, 1995 [doi>10.1109/MMCS.1995.484921]
• 4: D. Zhong, S-F. Chang, Structure Analysis of Sports Video Using Domain Models, ICME 2001, pp 713--716.
• 5: G. Sudhir , John C. M. Lee , Anil K. Jain, Automatic Classification of Tennis Video for High-level Content-based Retrieval, Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98), p.81, January 03-03, 1998
• 6: Baoxin Li , M. Ibrahim Sezan, Event Detection and Summarization in Sports Video, Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01), p.132, December 14-14, 2001
• 7: S. Raaijmakers, et al, Multimodal topic segmentation and classification of news video, ICME 2002, Vol 2, pp 33--36.
• 8: Mário Figueiredo , José M. N. Leitão , Anil K. Jain, On Fitting Mixture Models, Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, p.54-69, July 26-29, 1999
• 9: S. Kullback, Information Theory and Statistics, J. Wiley & Sons, New York, 1959.
• 10: Mike West , Jeff Harrison, Bayesian forecasting and dynamic models (2nd ed.), Springer-Verlag New York, Inc., New York, NY, 1997
• 11: Jianping Fan , Hangzai Luo , Xiaodong Lin, Semantic video classification by integrating flexible mixture model with adaptive EM algorithm, Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval, November 07-07, 2003, Berkeley, California [doi>10.1145/973264.973267]

INDEX TERMS

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

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Video analysis

General Terms:
Algorithms, Experimentation

Keywords:
concept drifting, incremental learning, video analysis, video semantic classification