The week's pick
Reseach Article
Nonparametric Video Retrieval and Frame Classification using Tiny Videos
| International Conference in Recent trends in Computational Methods, Communication and Controls |
| Foundation of Computer Science USA |
| ICON3C - Number 3 |
| April 2012 |
| Authors: A. K. M. Shanawas Fathima, R. Kanthavel |
| 375479b4-782b-4df9-8f9f-51e3d11ef72d |
A. K. M. Shanawas Fathima, R. Kanthavel . Nonparametric Video Retrieval and Frame Classification using Tiny Videos. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 3 (April 2012), 36-40.
Abstract
A nonparametric video retrieval and frame classification systm that uses affinity propagation algorithm is proposed. The main goal of the proposed system is to develop "tiny video" that achieves high video compression rates while retaining the overall visual appearance of video. The proposed video retrieval system utilizes the strengths of affinity propagation algorithm that uses exemplar based clustering to achieve a trade off between compression and video recall. By using this large collection of user labelled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The main applications of this proposed system is video copy detection and video recognotion
References
- G. Geisler and G. Marchionini, "The Open Video Project: A Research-Oriented Digital Video Repository," Proc. ACM Digital Libraries, pp. 258-259,, 2000.
- A. Hampapur, R. Jain, and T. E. Weymouth, "Production Model Based Digital Video Segmentation," Multimedia Tools Appl. , vol. 1, no. 1, pp. 9-46, 1995.
- A. Karpenko and P. Aarabi, "Tiny Videos: A Large Data Set for Nonparametric Video Retrieval and Frame Classification," IEEE transactions on Pattern analysis and machine intelligence, vol. 33, No. 3, March 2011.
- S. Lu, M. R. Lyu, and I. King, "Semantic Video Summarization Using Mutual Reinforcement Principle and Shot Arrangement Patterns," Proc. 11th IEEE CS Int'l Multimedia Modelling Conf. , pp. 60-67, 2005.
- D. Niste´r and H. Stewe´nius, "Scalable Recognition with a Vocabulary Tree," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2161-2168, 2006.
- B. Shahraray, "Scene Change Detection and Content-Based Sampling of Video Sequences," Proc. SPIE Conf. , pp. 2-13, Apr. 1995.
- N. Snavely, S. M. Seitz, and R. Szeliski, "Modeling the World from Internet Photo Collections," Int'l J. Computer Vision, vol. 80, no. 2, pp. 189-210, Nov. 2008.
- C. Toklu, S. P. Liou, and M. Das, "Videoabstract: A Hybrid Approach to Generate Semantically Meaningful Video Summaries," Proc. IEEE Int'l Conf. Multimedia and Expo, vol. 3, pp. 1333- 1336, 2000.
- A. Torralba, R. Fergus, and W. T. Freeman, "80 Million Tiny Images: A Large Data Set for Non-Parametric Object and Scene Recognition," Technical Report MIT-CSAIL-TR-2007-024, 2007.
- R. Zabih, J. Miller, and K. Mai, "A Feature-Based Algorithm for Detecting and Classifying Scene Breaks," Proc. ACM Multimedia Conf. , pp. 189-200, 1995.
- R. Zabih, J. Miller, and K. Mai, "A Feature-Based Algorithm for Detecting and Classifying Production Effects," Multimedia Systems, vol. 7, no. 2, pp. 119-128, 1999.
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