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Content based Video Retrieval using Enhance Feature Extraction

by Dipika H Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 19
Year of Publication: 2015
Authors: Dipika H Patel
10.5120/21173-4052

Dipika H Patel . Content based Video Retrieval using Enhance Feature Extraction. International Journal of Computer Applications. 119, 19 ( June 2015), 4-8. DOI=10.5120/21173-4052

@article{ 10.5120/21173-4052,
author = { Dipika H Patel },
title = { Content based Video Retrieval using Enhance Feature Extraction },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 19 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number19/21173-4052/ },
doi = { 10.5120/21173-4052 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:04:27.289507+05:30
%A Dipika H Patel
%T Content based Video Retrieval using Enhance Feature Extraction
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 19
%P 4-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Videos are a powerful and communicative media that can capture and present information. In recent times, large video databases are created because of the advancements in many video acquiring devices and Internet. A reliable system is needed to automate the process of this large amount of data. Content-based video retrieval has attracted extensive research during the decades. There are various models used for video retrieval. Content Based Video Retrieval is one model for retrieval of videos. Different users have different results in their minds. These lead to the process of selecting, indexing and ranking the database according to the human visual perception. This paper reviews the recent research in content based video retrieval system. Also the paper focus on video structure analysis, like, frame extraction from video, key frame extraction, feature extraction using SURF, similarity measure, video indexing, and video browsing. This system retrieves similar videos based on local feature detector and descriptor called SURF (Speeded-Up Robust Feature). For image convolution SURF relies on integral images. In SURF we use Hessian matrix-based measure for the detector and a distribution-based descriptor. SURF can be computed and compared much faster with respect to repeatability, uniqueness and robustness. SURF is better than previous proposed methods as SIFT, PCA-SIFT, GLOH, etc. Finally the future scope in this system is specified.

References
  1. Yarmohammadi, H. ; Rahmati, M. ; Khadivi, S. , "Content based video retrieval using information theory," Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on , vol. , no. , pp. 214,218, 10-12 Sept. 2013
  2. Dyana, A. ; Subramanian, M. P. ; Das, S. , "Combining Features for Shape and Motion Trajectory of Video Objects for Efficient Content Based Video Retrieval," Advances in Pattern Recognition, 2009. ICAPR '09. Seventh International Conference on , vol. , no. , pp. 113,116, 4-6 Feb. 2009
  3. Chattopadhyay, C. ; Das, S. , "STAR: A Content Based Video Retrieval system for oving camera video shots," Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference on , vol. , no. , pp. 1,4, 18-21 Dec. 2013
  4. Asha, S. ; Sreeraj, M. , "Content Based Video Retrieval Using SURF Descriptor," Advances in Computing and Communications (ICACC), 2013 Third International Conference on , vol. , no. , pp. 212,215, 29-31 Aug. 2013
  5. Jianshu Chao; Al-Nuaimi, A. ; Schroth, G. ; Steinbach, E. , "Performance comparison of various feature detector-descriptor combinations for content-based image retrieval with JPEG-encoded query images,"Multimedia Signal Processing (MMSP), 2013 IEEE 15th International Workshop on , vol. , no. , pp. 029,034, Sept. 30 2013-Oct. 2 2013
  6. Herbert Bay; Andress Ess, Tinne Tuytelaars,Luc Van Gool, "Speeded-Up robust features (SURF)" Vol. 110, No. 3, pp. 346--359, June 2008.
  7. Jing Fu, Xiaojun Jing, Songlin Sun, Yueming Lu, Ying Wang," C-SURF: Colored Speeded Up Robust Features", International Conference, I SCTCS 2012, Beijing, China, Volume 320, pp 203-210. May 28 – June 2, 2012
  8. Jin Zhao; Sichao Zhu; Xinming Huang, "Real-time traffic sign detection using SURF features on FPGA," High Performance Extreme Computing Conference (HPEC), 2013 IEEE , vol. , no. , pp. 1,6, 10-12 Sept. 2013
  9. H. Bay, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up Robust Features", Computer Vision–ECCV 2006.
  10. Weiming Hu; Nianhua Xie; Li Li; Xianglin Zeng; Maybank, S. , "A Survey on Visual Content-Based Video Indexing and Retrieval," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on , vol. 41, no. 6, pp. 797-819, Nov. 2011
  11. Y. -F. Ma, X. -S. Hua, L. Lu, and H. -J. Zhang, "A generic framework of user attention model and its application in video summarization," IEEE Trans. Multimedia, vol. 7, no. 5, pp. 907–919, Oct. 2005.
  12. K. W. Sze, K. M. Lam, and G. P. Qiu, "A new key frame representation for video segment retrieval," IEEE Trans. Circuits Syst. Video Technol. , vol. 15, no. 9, pp. 1148–1155, Sep. 2005.
  13. B. T. Truong and S. Venkatesh, "Video abstraction: A systematic review and classification," ACM Trans. Multimedia Comput. , Commun. Appl. , vol. 3, no. 1, art. 3, pp. 1–37, Feb. 2007.
  14. D. Besiris, F. Fotopoulou, N. Laskaris, and G. Economou, "Key frame extraction in video sequences: A vantage points approach," in Proc. IEEE Workshop Multimedia Signal Process. , Athens, Greece, Oct. 2007, pp. 434–437.
  15. D. P. Mukherjee, S. K. Das, and S. Saha, "Key frame estimation in video using randomness measure of feature point pattern," IEEE Trans. Circuits Syst. Video Technol. , vol. 7, no. 5, pp. 612–620, May 2007.
  16. Jing Li _, Nigel M. Allinson , "A comprehensive review of current local features for computer vision", Elsevier Neurocomputing, 2008 Elsevier B. V.
  17. S. Huang, C. Cai, F. Zhao, "An Efficient Wood Image Retrieval using SURF Descriptor", Proc. International Conference on Test and Measurement, 2009, pp. 55- 58, doi: 978-1-4244-4700-8/09.
  18. Zhang Y J, Lu H B, "Hierarchical video organization based on compact representation of video units". Proc. Workshop on Very Low Bitrates Video'99, 1999: 67-70.
  19. Calic J, Izquierdo E, "Efficient key-frame extraction and video analysis". Proceeding of the International Conference on Information Technology: Coding and Computing (ITCC'02), 2002: 28-33.
  20. He Xiang, Lu Gung-ho, "Algorithm of key frame extraction based on image similarity". Fujian Computer, 2009, 5: 73-74.
  21. Ding Hong-li, Chen Huai-xin, "Key frame extraction algorithm based on shot content change ratio". Computer Engineering, 2009, 13: 225-231.
Index Terms

Computer Science
Information Sciences

Keywords

Frame extraction Video retrieval Feature extraction Feature matching SURF C-SURF Video browsing.

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