Abstract
In this paper, we present an effective and efficient framework for baseball video scene classification. The results of scene classification can be able to provide the ground for baseball video abstraction and high-level event extraction. In general, most conventional approaches are shot-based, which shot change detection and key-frame extraction are necessary prerequisite procedures. On the contrary, we propose a frame-based approach. In our scene classification framework, an efficient playfield segmentation technique is proposed, and then the reduced field maps are utilized as scene templates. Because the shot change detection and the key-frame extraction are not required in proposed method, the new framework is very simple and efficient. The experimental results have demonstrated that the effectiveness of our proposed framework for baseball videos scene classification, and it can be easily extended the template-based approach to other kinds of sports videos.
Similar content being viewed by others
References
Barnard M, Odobez JM (2004) Robust Playfield Segmentation using MAP Adaptation. Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on 3:610–613
Buzo A, Gray AH, Gray RM, Markel JD (1980) Speech coding based upon vector quantization. Acoust Speech Signal Process, IEEE Trans 28(5):562–574
Chen HT, Hsiao MH, Chen HS, Tsai WJ, Lee SY (2008) A baseball exploration system using spatial pattern recognition. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS 2008) 3522–3525
Cheng HD, Jiang XH, Wang J (2002) Color image segmentation based on homogram thresholding and region merging. Pattern Recognit 35(2):373–393
Chu WT, Wu JL (2006) Development of Realistic Applications Based on Explicit Event Detection in Broadcasting Baseball Videos. Multi-Media Modelling Conference Proceedings, 2006 12th International Conference 12–19 Jan
Duan LY, Xu M, Tian Q, Xu CS, Jin JS (2005) A unified framework for semantic shot classification in sports video. Multimed, IEEE Trans 7(6):1066–1083
Ekin A, Tekalp AM, Mehrotra R (2003) Automatic soccer video analysis and summarization. Image Process, IEEE Trans 12(7):796–807
Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181
Foley JD, Dam AV, Feiner SK, Hughes JF (1990) Computer graphics: principles and practice. Addison-Wesley, Reading
Giakoumis I, Nikolaidis N, Pitas I (2006) Digital image processing techniques for the detection and removal of cracks in digitized paintings. Image Processing, IEEE Transactions 15(1):178–188
Hanjalic A, Zhang HJ (1999) An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis. IEEE Trans Circuits Syst Video Technol 9(8):1280–1289
Heikkilä J, Silvén O (2004) A real-time system for monitoring of cyclists and pedestrians. Image Vis Comput 22(7):563–570
Hua W, Han M, Gong Y (2002) Baseball scene classification using multimedia features. Multimedia and Expo, 2002. ICME ’02. Proceedings. 2002 IEEE International Conference on 1:821–824
Huang YR, Kuo CM, Hsieh CH, Pai CY (2004) Integrating Region Distribution and Edge Detection for Color Image Segmentation. Proceedings of the International Computer Symposium (ICS2004) 777–782 Dec
Huang CL, Shih HC, Chao CY (2006) Semantic analysis of soccer video using dynamic Bayesian network. Multimed, IEEE Trans 8(4):749–760
Hung MH, Hsieh CH, Jian JL (2005) Scene classification for baseball sport videos. IEEE Int. Confer on System and Signals (ICSS2005) 254–257
Jian JL, Hung MH, Hsieh CH, Chang Y (2005) Real-time scene classification for baseball videos. 18th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP2005) 115–122 Aug
Kangas JA, Kohonen TK, Laaksonen JT (1990) Variants of self-organizing maps. IEEE Trans Neural Netw 1(1):93–99
Kohonen T (1990) The self-organization map. Proceedings of the IEEE 78(9):1464–1480
Leonardi R, Migliorati P, Prandini M (2004) Semantic indexing of soccer audio-visual sequences: a multimodal approach based on controlled Markov chains. IEEE Trans Circuits Syst Video Technol 14(5):634–643
Lien CC, Chiang CL, Lee CH (2007) Scene-based event detection for baseball videos. J Vis Commun Image Represent 18:1–14
Liu T, Zhang HJ, Qi F (2003) A novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans Circuits Syst Video Technol 13(10):1006–1013
Lu H, Tan YP (2003) An unsupervised approach to dominant video scene clustering. Circuits and Systems. Proceedings of the 2003 International Symposium (ISCAS ’03) on 2:680–683 May
Montoya MG, Gil C, Garcia I (2003) The load unbalancing problem for region growing image segmentation algorithms. J Parallel Distrib Comput 63(4):387–395
Pei SC, Chen F (2003) Semantic scenes detection and classification in sports videos. The 16th IPPR Conference on Computer Vision, Graphics and Image Processing ( CVGIP2003) 210–217
Sze KW, Lam KM, Qiu G (2005) A new key frame representation for video segment retrieval. IEEE Trans Circuits Syst Video Technol 15(9):1148–1155
Wang L, Zeng B, Lin S, Xu G, Shum HY (2004) Automatic extraction of semantic colors in sports video. Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on 3:617–620 May
Xie L, Xu P, Chang SF, Divakaran A, Sun H (2004) Structure analysis of soccer video with domain knowledge and hidden markov models. Pattern Recognit Lett 25(7):767–775
Xu C, Prince JL (1998) Snakes, shapes and gradient vector flow. Image Processing, IEEE Transactions 7(3):359–369
Zhu S, Liu Y (2009) Video scene segmentation and semantic representation using a novel scheme. Multimedia Tools Appl 42:183–205. doi:10.1007/s11042-008-0233-0
Acknowledgement
The authors would like to express their sincere thanks to the anonymous reviewers for their invaluable comments and suggestions. This work was supported by the National Science Counsel of Republic of China Granted NSC 98-2221-E-214-054-
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kuo, CM., Chang, WH., Fang, MY. et al. A template-based baseball video scene classification using efficient playfield segmentation. Multimed Tools Appl 55, 399–422 (2011). https://doi.org/10.1007/s11042-010-0555-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-010-0555-6