Integration of MapReduce with an Interactive Boosting Mechanism for Image Background Subtraction in Cultural Sightseeing

详细信息    查看全文

• 作者：Sheng-Tzong Cheng (21)
  Yin-Jun Chen (21)
  Yu-Ting Wang (21)
  Chen-Fei Chen (22)
  
• 关键词：Cloud computing ; Background subtraction ; Boosting learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8390
• 期：1
• 页码：180-191
• 全文大小：3,259 KB
• 参考文献：1. KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proceedings of European Workshop Advanced Video Based Surveillance Systems (2001)
  2. Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28, 657鈥?62 (2006) CrossRef
  3. Piccardi, M.: Background subtraction techniques: a review. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099鈥?104 (2004)
  4. Sun, J., Zhang, W., Tang, X., Shum, H.-Y.: Background cut. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 628鈥?41. Springer, Heidelberg (2006) CrossRef
  5. Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309鈥?14 (2004) CrossRef
  6. Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 105鈥?12 (2001)
  7. White, B., Yeh, T., Lin, J., Davis, L.: Web-scale computer vision using MapReduce for multimedia data mining. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, pp. 1鈥?0 (2010)
  8. Wan, C., Wang, C., Zhang, K.: MRKDSBC: a distributed background modeling algorithm based on MapReduce. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part I. LNCS, vol. 7367, pp. 668鈥?77. Springer, Heidelberg (2012) CrossRef
  9. Almeer, H.M.: Cloud hadoop map reduce for remote sensing image analysis. J. Emerg. Trends Comput. Inf. Sci. 13(4), 637鈥?44 (2012)
  10. Stauffer, C., Grimson, W. E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE International Conference on Computer Vision Pattern Recognition, pp. 246鈥?52, Jun 1999
  11. Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751鈥?67. Springer, Heidelberg (2000) CrossRef
  
• 作者单位：Sheng-Tzong Cheng (21)
  Yin-Jun Chen (21)
  Yu-Ting Wang (21)
  Chen-Fei Chen (22)
  
  21. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
  22. Department of Information and Communications, Shih Hsin University, Taipei, Taiwan
  
• 丛书名：Advances in Web-Based Learning 峋縄CWL 2013 Workshops
• ISBN：978-3-662-46315-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Background subtraction is widely used in multimedia applications, such as traffic monitoring, video surveillance, and object tracking. Several methods with different advantages in different applications have been proposed. The advent of cloud computing also has made possible of the combination of various background subtraction techniques and the processing of large amounts of images. In this paper, an integrated algorithm for background subtraction is implemented and analyzed. The proposed AdaBoost algorithm combines weak classifiers: pixel-based background subtraction methods, block-based background subtraction methods, and graph-cut segmentation methods. After training, the program adjusts the weight of each weak classifier. The algorithm is accelerated using Hadoop cloud-computing architecture. By using a MapReduce framework, this system can parallel-processing on multiple servers in order to reduce computing time. When the system completes its task, the user can see the combined results on the screen and then choose the preferred result. The system can obtain user feedback and tune the combination mechanism.