Anand Paul
Abstract
In this study, we present a video search and indexing system based on the state support vector (SVM) network, video graph, and reinforcement agent for recognizing and organizing video events. In order to enhance the recognition performance of the state SVM network, two innovative techniques are presented: state transition correction and transition quality estimation. The classification results are also merged into the video indexing graph, which facilitates the search speed. A reinforcement algorithm with an efficient scheduling scheme significantly reduces both the power consumption and time. The experimental results show the proposed state SVM network was able to achieve a precision rate as high as 83.83% and the query results of the indexing graph reached 80% accuracy. The experiments also demonstrate the performance and feasibility of our system.
- Benini, L., Hodgson, R., and Siegel, P. 1998. System-level power estimation and optimization. In Proceedings of the International Symposium on Low Power Electronics and Design. 173--178. Google Scholar
Digital Library
- Benini, L., Paleologo, G., Bogliolo, A., and de Micheli, G. 1999. Policy optimization for dynamic power management. IEEE Trans. Comput.-Aided Desi. 18, 6, 813--833. Google ScholarDigital Library
- Chen, B.-W., Wang, J.-C., and Wang, J.-F. 2009a. A novel video summarization based on mining the story-structure and semantic relations among concept entities. IEEE Trans. Multimed. 11, 2, 295--312. Google ScholarDigital Library
- Chen, C.-Y., Wang, T.-C., Wang, J.-F., and Shieh, L.-P. 2009b. SVM-based state transition framework for dynamical human behavior identification. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE Computer Society, 1933--1936. Google ScholarDigital Library
- Chung, E., Benini, L., and de Micheli, G. 1999a. Dynamic power management using adaptive learning trees. In Proceedings of the International Conference on Computer-Aided Design. Google ScholarDigital Library
- Chung, E., Benini, L., and de Micheli, G. 1999b. Dynamic power management for non-stationary service requests. In Proceedings of the Symposium on Design, Automation, and Test in Europe. 77--81. Google ScholarDigital Library
- Costa Ribeiro, C. H. 2002. A tutorial on reinforcement learning techniques. Department of Theory of Computation Technological Institute of Aeronautics São Jos e dos Campos, Brazil.Google Scholar
- Ganapathiraju, A., Hamaker, J. E., and Picone, J. 2004. Applications of support vector machines to speech recognition. IEEE Trans. Sig. Proc. 52, 8, 2348--2355. Google ScholarDigital Library
- Golding, R., Bosch, P., and Wilkes, J. 1996. Idleness is not sloth: HP Laboratories Tech. Rep. HPL-96--140.Google Scholar
- Huang, X., Acero, A., and Hon, H.-W. 2001. Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice-Hall, New York, NY. Google ScholarDigital Library
- Huang, C.-L., Shih, H.-C., and Chao, C.-Y. 2006. Semantic analysis of soccer video using dynamic Bayesian network. IEEE Trans. Multimed. 8, 4, 749--760. Google ScholarDigital Library
- Irani, S., Shukla, S., and Gupta, R. 2002. Competitive analysis of dynamic power management strategies for systems with multiple power saving state. In Proceedings of the Design Automation and Test Conference Europe (DATE02). Google ScholarDigital Library
- Li, Y., Tian, Y., Duan, L.-Y., Yang, J., Huang, T., and Gao, W. 2010. Sequence multi-labeling: A unified video annotation scheme with spatial and temporal context. IEEE Trans. Multimed. 12, 8, 814--828. Google ScholarDigital Library
- Lu, Y., Chung, E., Simunic, T., Benini, L., and de Micheli, G. 2000. Quantitative comparison of PM Algorithms. Design, Automation and Test in Europe, pp. 20--26. Google ScholarDigital Library
- Lu, Y., Simunic, T., and de Micheli, G. 1999a. Software controlled power management. In Proceedings of the 7th International Workshop on Hardware/Software Codesign. 157--161. Google ScholarDigital Library
- Paul, A., Jiang, Y. C., and Wang, J. F. 2010. Computation aware scheme for visual signal processing, J. Softw.Google Scholar
- Ren, Z., Krogh, B. H., and Marculescu, R. 2005. Hierarchical adaptive dynamic power management. IEEE Trans. Comput. Google ScholarDigital Library
- Shih, E., Bahl, V., and Sinclair, M. 2003. Reducing energy consumption of Wireless, Mobile Devices Using a Secondary Low-Power Channel, MIT Laboratory for Computer Science, March.Google Scholar
- Shukla, S. and Gupta, R. 2001. A model checking approach to evaluating system level power management for embedded systems. In Proceedings of IEEE Workshop on High Level Design Validation and Test (HLDVT 01). IEEE Press. Google ScholarDigital Library
- Simunic, T. 2000. Dynamic management of power consumption HP Labs, Tech. Rep.Google Scholar
- Simunic, T., Bennin, L., and de Micheli, G. 1999. Event-driven power management. In Proceedings of the International Symposium on System Synthesis, pp. 18--23. Google ScholarDigital Library
- Simunic, T., Benini, L., and de Micheli, G. 2000. Power management of laptop hard disk. Design, Automation, and Test in Europe, p. 736.Google Scholar
- Srinivasan, P. and Shi, J. 2007. Bottom-up recognition and parsing of the human body. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 1--8. Google ScholarDigital Library
- Sutton, R. S. and Barto, A. G. 1998. Reinforcement learning -- An introduction, MITPress, Cambridge, MA. Google ScholarDigital Library
- Takahashi, M., Fujii, M., and Yagi, N. 2008. Automatic pitch type recognition from baseball broadcast videos. In Proceedings of the 10th IEEE International Symposium on Multimedia. IEEE Computer Society, 15--22. Google ScholarDigital Library
- Tang, J., Hua, X.-S., Qi, G.-J., Song, Y., and Wu, X. 2008. Video annotation based on kernel linear neighborhood propagation. IEEE Trans. Multimed. 10, 4, 620--628. Google ScholarDigital Library
- Xie, L., Xu, P., Chang, S.-F., Divakaran, A., and Sun, H. 2004. Structure analysis of soccer video with domain knowledge and hidden Markov models. Patt. Recog. Lett. 25, 7, 767--775. Google ScholarDigital Library
- Xu, G., Ma, Y.-F., Hong-Jiang, Z., and Yang, S.-Q. 2005. An HMM-based framework for video semantic analysis. IEEE Trans. Circu. Syst. Video Tech. 15, 11, 1422--1433. Google ScholarDigital Library
- Zhong, L. and Jha, N. K. 2004. Dynamic power optimization of interactive systems, In Proceedings of the IEEE International Conference on VLSI Design. Google ScholarDigital Library
- Zhong, L. and Jha, N. K. 2006. Dynamic power optimization targeting user delays in interactive systemes. IEEE Trans. Mobile Computing. Google ScholarDigital Library
Index Terms
- Video search and indexing with reinforcement agent for interactive multimedia services
Recommendations
A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval
Effective video retrieval is the result of interplay between interactive query selection, advanced visualization of results, and a goal-oriented human user. Traditional interactive video retrieval approaches emphasize paradigms, such as query-by-keyword ...
Reranking Methods for Visual Search
Most semantic video search methods use text-keyword queries or example video clips and images. But such methods have limitations. Here the authors introduce two reranking processes for image and video search that automatically reorder results from ...
Adding Semantics to Detectors for Video Retrieval
In this paper, we propose an automatic video retrieval method based on high-level concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. ...
Comments