skip to main content
10.1145/2791405.2791499acmotherconferencesArticle/Chapter ViewAbstractPublication PageswciConference Proceedingsconference-collections
research-article

Gabor Moments Based Shot Boundary Detection

Authors Info & Claims
Published:10 August 2015Publication History

ABSTRACT

In this paper, we present a shot boundary detection method based on Gabor transformed first order moments. Unlike other approaches where gray scale images are considered for processing with huge feature space, we have worked in different colour spaces and found that only first order moments are good enough to capture the shot boundaries. In the proposed method, each frame of a given video is convolved with six differently oriented Gabor filter under each channel of a colour component and first order moments were computed which were used for shot boundary detection task. The subset of TRECVID 2001 dataset is considered for experimentation to evaluate the performance of the proposed method. A comparative analysis with some of the existing algorithms is also presented. In addition, we have conducted experimentation in different colour spaces to identify the suitable colour space in Gabor transformed domain for shot boundary detection task.

References

  1. D. Adjeroh, M. Lee, N. Banda, and U. Kandaswamy. Adaptive edge-oriented shot boundary detection. EURASIP Journal on Image and Video Processing, 2009(1):859371, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. Barbu. Novel automatic video cut detection technique using gabor filtering. Computers & Electrical Engineering, 35(5):712--721, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Z. Cernekova, I. Pitas, and C. Nikou. Information theory-based shot cut/fade detection and video summarization. Circuits and Systems for Video Technology, IEEE Transactions on, 16(1):82--91, Jan 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Cooper, T. Liu, and E. Rieffel. Video segmentation via temporal pattern classification. Multimedia, IEEE Transactions on, 9(3):610--618, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. Dugad, K. Ratakonda, and N. Ahuja. Robust video shot change detection. In Multimedia Signal Processing, 1998 IEEE Second Workshop on, pages 376--381, Dec 1998.Google ScholarGoogle ScholarCross RefCross Ref
  6. U. Gargi, R. Kasturi, and S. H. Strayer. Performance characterization of video-shot-change detection methods. Circuits and Systems for Video Technology, IEEE Transactions on, 10(1):1--13, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. W. Hu, N. Xie, L. Li, X. Zeng, and S. Maybank. A survey on visual content-based video indexing and retrieval. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(6):797--819, Nov 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. K. Kamarainen, V. Kyrki, and H. Kalviainen. Invariance properties of gabor filter-based features-overview and applications. Trans. Img. Proc. 15(5):1088--1099, May 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Y. Kawai, H. Sumiyoshi, and N. Yagi. Shot boundary detection at trecvid 2007. In TRECVID, 2007.Google ScholarGoogle Scholar
  10. O. Küçüktunç, U. Güdükbay, and O. Ulusoy. Fuzzy color histogram-based video segmentation. Comput. Vis. Image Underst., 114(1):125--134, Jan. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. G. Lakshmi Priya and S. Domnic. Walsh hadamard transform kernel-based feature vector for shot boundary detection. Image Processing, IEEE Transactions on, 23(12):5187--5197, Dec 2014.Google ScholarGoogle Scholar
  12. W.-K. Li and S.-H. Lai. Integrated video shot segmentation algorithm. In Electronic Imaging, pages 264--271, 2003.Google ScholarGoogle Scholar
  13. Y.-N. Li, Z.-M. Lu, and X.-M. Niu. Fast video shot boundary detection framework employing pre-processing techniques. Image Processing, IET, 3(3):121--134, 2009.Google ScholarGoogle Scholar
  14. S. Lian. Automatic video temporal segmentation based on multiple features. Soft Computing, 15(3):469--482, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M.-H. Park, R.-H. Park, and S. W. Lee. Shot boundary detection using scale invariant feature matching. In Electronic Imaging, pages 60771N--60771N, 2006.Google ScholarGoogle Scholar
  16. G. Priya and S. Domnic. Transition detection using hilbert transform and texture features. American J. of Signal Proc. 10, pages 35--40, 2012.Google ScholarGoogle Scholar
  17. H. Tahvilian, P. Moallem, and A. Monadjemi. Balloon energy based on parametric active contour and directional walsh--hadamard transform and its application in tracking of texture object in texture background. EURASIP Journal on Advances in Signal Processing, 2012(1):1--15, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  18. S. Wang, Y. Xia, Q. Liu, J. Luo, Y. Zhu, and D. D. Feng. Gabor feature based nonlocal means filter for textured image denoising. J. Vis. Comun. Image Represent., 23(7):1008--1018, Oct. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. H.-W. Yoo, H.-J. Ryoo, and D.-S. Jang. Gradual shot boundary detection using localized edge blocks. Multimedia Tools and Applications, 28(3):283--300, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. Zhang, A. Kankanhalli, and S. W. Smoliar. Automatic partitioning of full-motion video. Multimedia systems, 1(1):10--28, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. S.-R. Zhou, J.-P. Yin, and J.-M. Zhang. Local binary pattern (lbp) and local phase quantization (lbq) based on gabor filter for face representation. Neurocomputing, 116(0):260--264, 2013.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Gabor Moments Based Shot Boundary Detection

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Other conferences
            WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics
            August 2015
            763 pages
            ISBN:9781450333610
            DOI:10.1145/2791405

            Copyright © 2015 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 10 August 2015

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

            Acceptance Rates

            WCI '15 Paper Acceptance Rate98of452submissions,22%Overall Acceptance Rate98of452submissions,22%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader