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A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification

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Abstract

Soccer video summarization and classification is becoming a very important topic due to the world wide importance and popularity of soccer games which drives the need to automatically classify video scenes thus enabling better sport analysis, refereeing, training, advertisement, etc. Machine learning has been applied to the task of sports video classification. However, for some specific image and video problems (like sports video scenes classification), the learning task becomes convoluted and difficult due to the dynamic nature of the video sequence and the associated uncertainties relating to changes in light conditions, background, camera angle, occlusions and indistinguishable scene features, etc. The majority of previous techniques (such as SVM, neural network, decision tree, etc.) applied to sports video classifications did not provide a consummate solution, and such models could not be easily understood by human users; meanwhile, they increased the complexity and time of computation and the associated costs of the involved standalone machines. Hence, there is a need to develop a system which is able to address these drawbacks and handle the high levels of uncertainty in video scenes classification and undertake the heavy video processing securely and efficiently on a cloud computing based instance. Hence, in this paper we present a cloud computing based multi classifier systems which aggregates three classifiers based on neural networks and two fuzzy logic classifiers based on type-1 fuzzy logic and type-2 fuzzy logic classification systems which were optimized by a Big-Bang Big crunch optimization to maximize the system performance. We will present several real world experiments which shows the proposed classification system operating in real-time to produce high classification accuracies for soccer videos which outperforms the standalone classification systems based on neural networks, type-1 and type-2 fuzzy logic systems.

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Notes

  1. PriceWaterhouseCoopers (2015). Changing the game outlook for the global sports market [online]. Available at: https://www.pwc.com/gx/en/hospitality-leisure/pdf/changing-the-game-outlook-for-the-global-sports-market-to-2015.pdf [last accessed 15/10/2015].

  2. Qualys Guard: https://www.qualys.com/enterprises/qualysguard/.

  3. Open Security Architecture, 2009, http://www.opensecurityarchitecture.org/cms/.

  4. UK Premier League (2015). Chelsea vs. Man City, 31st Jan 2015; Chelsea vs. Liverpool, 28th Jan 2015; Manchester City vs. West Bromwich Albion, 4th May 2015 [online]. Available at: http://www.zhiboba.cc/zuqiu.html [last accessed 15/5/2015].

References

  1. Ekin A, Tekalp A, Mehrotra R (2003) Automatic Soccer video analysis and summarization. IEEE Trans Image Process 12(7):796–807

    Article  Google Scholar 

  2. Dai J, Duan L, Tong X, Xu C (2005) Replay scene classification in Soccer video using web broadcast text. In: Proceedings of the IEEE international conference on multimedia and expo, 6–8 July 2005, Amsterdam (ICME 2005), pp 1098–1101

  3. Zhu G, Huang Q, Xu C, Xing L, Gao W, Yao H (2007) Human behavior analysis for highlight ranking in broadcast racket sports video. IEEE Trans Multimed 9(6):1167–1182

    Article  Google Scholar 

  4. Zhu G, Xu C, Huang Q, Gao W (2006) Automatic multi-player detection and tracking in broadcast sports video using support vector machine and particle filter. In: Proceedings of the IEEE international conference on multimedia and expo, Toronto, 9–12 July 2006, pp 1629–1632

  5. Zhu G, Xu C, Huang Q, Rui Y, Jiang S, Gao W, Yao H (2009) Event tactic analysis based on broadcast sports video. IEEE Trans Multimed 11(1):49–67

    Article  Google Scholar 

  6. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li F (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of computer vision and pattern recognition (CVPR), 23–28 June 2014, Columbus, OH, USA, pp 1725–1732

  7. Boutell MR, Luo J, Shen X, Brown C (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771

    Article  Google Scholar 

  8. Bagheri-Khaligh A, Raziperchikolaei R, Ebrahimi Moghaddam M (2012) A new method for shot classification in soccer sports video based on SVM classifier. In: Proceedings of the IEEE southwest symposium on image analysis and interpretation (SSIAI), Santa Fe, NM, USA, 22–24 April, pp 109–112

  9. Alipour S, Oskouie P, Eftekhari-Moghadam A-M (2012) Bayesian belief based tactic analysis of attack events in broadcast soccer video. In: Proceedings of the international conference on informatics, electronics and vision (ICIEV), Dhaka, pp 612–617

  10. Verma B, Rahman A (2012) Cluster-oriented ensemble classifier: impact of multicluster characterization on ensemble classifier learning. IEEE Trans Knowl Data Eng 24(4):605–618

    Article  Google Scholar 

  11. Liu J, Mohandes M, Deriche M (2013) A multi-classifier image based vacant parking detection system. In: Proceedings of the international conference on electronics, circuits, and systems (ICECS), Abu Dhabi, 8–11 Dec, pp 1–4

  12. Sigari M, Sureshjani S, Soltanian-Zadeh H (2011) Sport Video Classification Using an Ensemble Classifier. In: Proceedings of the Iranian conference on machine vision and image processing, Tehran, 16–17 Nov, pp 1–4

  13. Hagras H (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans Fuzzy Syst 12(4):524–539

    Article  Google Scholar 

  14. Hagras H, Lynch C, Callaghan V (2006) Embedded interval type-2 neuro-fuzzy speed controller for marine diesel engines. In: Proceedings of the international conference on information processing and management of uncertainty in knowledge-based systems (IPMU 2006), Paris, France, pp 1340–1347

  15. Ishibuchi H, Yamamoto T (2005) Rule weight specification in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 13(4):428–435

    Article  Google Scholar 

  16. Hagras H, Packham I, Vanderstockt Y, Mcnulty N, Vadher A, Doctor F (2008) An intelligent agent based approach for energy management in commercial buildings. In: Proceedings of the 2008 IEEE international conference on fuzzy systems, Hong Kong, pp 156–162

  17. Hagras H (2007) Embedding computational intelligence in pervasive spaces. IEEE Pervasive Comput 6(3):85–89

    Article  Google Scholar 

  18. Mendel JM (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  MathSciNet  Google Scholar 

  19. Erol O, Eksin I (2006) A new optimization method: Big Bang–Big Crunch. Adv Eng Softw 37(2):106–111

    Article  Google Scholar 

  20. Kumbasar T, Eksin I, Guzelkaya M, Yesil E (2011) Adaptive fuzzy model based inverse controller design using BB–BC optimization algorithm. expert syst appl 38(10):12356–12364

    Article  Google Scholar 

  21. Garcia-Valverde T, Garcia-Sola A, Hagras H (2012) A fuzzy logic-based system for indoor localization using WiFi in ambient intelligent environments. IEEE Trans Fuzzy Syst 21(4):702–718

    Article  Google Scholar 

  22. Bezdeck J, Ehrlich R, Full W (1984) FCM: the fuzzy C-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

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Authors and Affiliations

  1. The Computational Intelligence Centre, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Song Wei & Hani Hagras

  2. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Daniyal Alghazzawi

Authors
  1. Song Wei
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  2. Hani Hagras
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  3. Daniyal Alghazzawi
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Correspondence to Hani Hagras.

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Wei, S., Hagras, H. & Alghazzawi, D. A cloud computing based Big-Bang Big-Crunch fuzzy logic multi classifier system for Soccer video scenes classification. Memetic Comp. 8, 307–323 (2016). https://doi.org/10.1007/s12293-016-0207-0

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