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Content based Video Querying Technique for Video Retrieval and Video Making from Large Video Compilation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 106 - Number 6 |
| Year of Publication: 2014 |
| Authors: N. Sudha Bhuvaneswari, M. Madhanika |
10.5120/18524-9719
|
N. Sudha Bhuvaneswari, M. Madhanika . Content based Video Querying Technique for Video Retrieval and Video Making from Large Video Compilation. International Journal of Computer Applications. 106, 6 ( November 2014), 13-19. DOI=10.5120/18524-9719
Abstract
Content based video querying and video matching systems are popular in the recent technology. The content based video querying takes a sample video clip as an input query and performs the searching operation in the collection of videos which are stored in the video database. This proposal, introduces a novel content-based video matching and copy elimination system that finds the most relevant video segments from video database based on the given query video clip. For effective video copy elimination based on the feature extraction the proposed system applies the scheme names as Dense SIFT_OP (DSIFT_OP). This performs the feature extraction, copy elimination and effective query matching from the video collections. This thesis overcomes the problem of video frame mining based on effective Meta information's and semantic similarity measures. The semantic similarity contains both textual and visual similarity measures. According to the discovered features and patterns, the query frame can obtain a set of relevant video frames in the refinement process. The proposed approach robustly identifies the duplicate frames and alignsthe extracted frames, which containing the significant spatial and temporal differences. Based on the feature extraction algorithm and semantic feature identification this applies a motion matching alignment scheme image alignment and video making with extracted clips in the large video database framework. For image analysis and synthesis the image information is transferred from the nearest neighbors to a queryimage according to the distance. This framework is demonstrated through concrete applications, such as motion field prediction and pattern analysis from a single image, pattern synthesis via object transfer, image registration and object recognition. The proposed sequence of object and distance finding yields better result for video making and video copy elimination
References
- Amir, G. Iyengar, J. Argillander, M. Campbell, A. Haubold, S. Ebadollahi, F. Kang, M. R. Naphade, A. Natsev, J. R. Smith, J. Tesic, and T. Volkmer, "IBM research trecvid-2005 video retrieval system," in Proc. TRECVID Workshop, Washington, DC, 2005.
- Anjewierden, A. , Koll¨offel, B. , and Hulshof C. , "Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes". International Workshop on Applying Data Mining in e-Learning, ADML'07, Vol-305, Page No 23-32Sissi,LassithiCrete Greece, 18 September, 2007.
- Chapman, P. , Clinton, J. , Kerber, R. , Khabaza, T. ,Reinartz, T. , Shearer, C. and Wirth, R. . . "CRISP-DM 1. 0 : Step-by-step data mining guide, NCR Systems Engineering Copenhagen (USA and Denmark), DaimlerChrysler AG (Germany), SPSS Inc. (USA) and OHRA Verzekeringenen BankGroup B. V (The Netherlands), 2000".
- L. Chen and F. W. M. Stentiford, "Video Sequence Matching Based on Temporal Ordinal Measurement," Pattern Recognition Letters, vol. 29, no. 13, pp. 1824-1831, Oct. 2008.
- R. Cheng, Z. Huang, H. T. Shen, and X. Zhou, "Interactive Near- Duplicate Video Retrieval and Detection," Proc. ACM Int'l Conf. Multimedia, pp. 1001-1002, 2009.
- Chenxia Wu, Jianke Zhu, Jiemi Zhang College of Computer Science, Zhejiang University, China. A Content-based Video Copy Detection Method with Randomly Projected Binary Features
- M. Christel and R. Yan, "Merging storyboard strategies and automatic retrieval for improving interactive video search," in Proc. Int. Conf. Image and Video Retrieval (CIVR), Amsterdam, The Netherlands, 2007.
- S. Dagtas, W. Al-Khatib, A. Ghafoor, and R. Kashyap, "Models for motion-based video indexing and retrieval," IEEE Trans. Image Process. , vol. 9, no. 1, pp. 88–101, Jan. 2000.
- M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni, "Locality-sensitive hashing scheme based on p-stable distributions," in Proc. 20thAnnu. Symp. Computational Geometry, New York, 2004, pp. 253–262.
- A. Hampapur and R. Bolle, "Comparison of Distance Measures for Video Copy Detection," Proc. IEEE Int'l Conf. Multimedia and Expo (ICME), pp. 188-192, 2001.
- A. Hampapur, K. Hyun, and R. Bolle, "Comparison of Sequence Matching Techniques for Video Copy Detection,"Proc. SPIE, Storage and Retrieval for Media Databases, vol. 4676, pp. 194-201, Jan. 2002
- C. Harris and M. Stephens, "A Combined Corner and Edge Detector," Proc. Fourth Alvey Vision Conf. , pp. 147-151, 1988.
- X. Wu, C. -W. Ngo, A. Hauptmann, and H. -K. Tan, "Real-Time Near-Duplicate Elimination for Web Video Search with Content and Context," IEEE Trans. Multimedia, vol. 11, no. 2, pp. 196-207, Feb. 2009.
- S. C. Hoi, R. Jin, and M. R. Lyu, "Learning non-parametric kernel matrices from pairwise constraints," in Proc. 24th Int. Conf. Machine Learning (ICML'07), OR, June 20–24, 2007.
- S. C. H. Hoi, R. Jin, and M. R. Lyu, "Large-scale text categorization by batch mode active learning," in Proc. 15th Int. World Wide Web conference (WWW'06), CITY?, U. K. , May 23–26, 2006.
- Hoi, Steven CH, and Michael R. Lyu. "A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval. " IEEE TRANSACTIONS ON MULTIMEDIA 10. 4 (2008): 607.
- Hong Liu, Hong Lu, Member, IEEE, and XiangyangXue, Member, IEEE
- Z. Huang, H. T. Shen, J. Shao, B. Cui, and X. Zhou, "Practical Online Near-Duplicate Subsequence Detection for Continuous Video Streams," IEEE Trans. Multimedia, vol. 12, no. 5, pp. 386-397, Aug. 2010.
- Ji Zhang Wynne Hsu Mong Li Lee Image Mining: Trends and developments.
- A. Joly, O. Buisson, and C. Frelicot, "Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search," IEEE Trans. Multimedia, vol. 9, no. 2, pp. 293-306, Feb. 2007.
- C. Kim and B. Vasudev, "Spatiotemporal Sequence Matching for Efficient Video Copy Detection," IEEE Trans. Circuits and Systems for Video Technology, vol. 15, no. 1, pp. 127-132, Jan. 2005.
- KuratThearling . Foundation of data mining www. thearling . com
- Kwang-Ting Cheng Dept. of Electrical and Computer Engineering University of California, Santa Barbara , CA 93106, USA.
- I. Laptev and T. Lindeberg, "Space-Time Interest Points," Proc. Int'l Conf. Computer Vision, pp. 432-439, 2003.
- Larose, D. T. , "Discovering Knowledge in Data: An Introduction to Data Mining", ISBN 0-471-66657-2, ohn Wiley & Sons, Inc, 2005.
- J. Law-To, C. Li, and A. Joly, "Video Copy Detection: A Comparative Study," Proc. ACM Int'l Conf. Image and Video Retrieval, pp. 371-378, July 2007.
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