The week's pick
Random Articles
Reseach Article
A Video Mining Application for Image Retrieval
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 20 - Number 3 |
| Year of Publication: 2011 |
| Authors: Lakshmi Rupa G., Gitanjali J. |
10.5120/2410-3214
|
Lakshmi Rupa G., Gitanjali J. . A Video Mining Application for Image Retrieval. International Journal of Computer Applications. 20, 3 ( April 2011), 46-51. DOI=10.5120/2410-3214
Abstract
Video mining involves the analysis of content-based classification, indexing, and retrieval; representation, browsing, and visualization of the features in the video. This paper mainly is to survey available and potential technologies for video monitoring and mining, the general methods of fast and efficient content-based analysis of video streams and to identify promising directions for research in this challenging area. This involves automatic detection of boundaries between the shots in a video and then those are indexed to form a library, saving the proper features of each shot/frame. This helps in the easy retrieval based on the shot according to the user requirements. Here, we present an automation technique for video indexing and creation of a digital library. A video digital library is build which is composed of stream shots and the wavelet coefficients for these shots. The wavelength coefficients are computed on the image and all the video frames/shots for a full search function in all the frames of the indexed video. This digital library system can be used for any number of shots or even any number of frames.
References
- D. Tsai and S. Lai, "Independent Component Analysis-Based Background Subtraction for Indoor Surveillance", IEEE Transactions On Image Processing, Vol. 18, No. 1, January 2009
- A. Smeulders, M.Worring, S. Santini, A. Gupta, and R. Jain, “Content based image retrieval at the end of the early years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349–1380, December 2000..
- M. Nixon Alberto S. Aguado, "Feature Extraction and Image Processing", Second edition, book Elsevier Ltd., 2008.
- Tao Li, Qi Li, Shenghuo Zhu, Mitsunori Ogihara, “A Survey on Wavelet Applications in Data Mining”, SIGKDD Explorations, Volume 4, Issue 2, Pg 49-68.
- K. Yoon, D. F. DeMenthon, and D. Doermann, “Event detection from MPEG video in the compressed domain,” in Int. Conf. on Pattern Recognition, Barcelona, Spain, 2000.
- Y.Alp Aslandogan and Clement T. Yu, “Techniques and Systems for Image and Video Retrieval”, IEEE Trans. On Knowledge and Data engineering, Vol.11, No. 1, 1999.
- V. Kobla, D.S. Doermann, and K-I. Lin, “Archiving, indexing and retrieval of video in compressed domain,” in SPIE Conference on Multimedia Storage and Archiving Systems, 1996, vol. 2916, pp. 78-89
- A. W. M. Smeulders et al., "Content-Based Image Retrieval at the End of the Early Years, "IEEE Transactions on Pattern Analysis and Machine Intelligence (2000): 1349-1380.
- M. S. Lew et al., "Content-based multimedia information retrieval: State of the art and challenges," ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 2, no. 1 (2006): 1-19.
- Aslandogan Y. and Yu, “Techniques and systems for Image and Video Retrieval”, IEEE TKDE. 1999, pp. 56-63.2002
- X. Zhu, W. Aref. J. Fan, A. Catlin, A. Elmagarmid, “Medical Video Mining for Efficient Database Indexing, Management and Access”, Proc. Of ICDE, 2003
- S. Newsam, J. Tesic, L. Wang, and B.S. Manjunath, “Mining Images and Video,” Proc. DIMACS Workshop on Video Mining.
Index Terms
Keywords