A Comparative Discussion on Various Modern Video Retrieval Strategies

  • K. Mallikharjuna LingamEmail author
  • V. S. K. Reddy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)


In the recent past, wide ranges of video retrieval processes were presented by different researchers. In order to boost the ease of access of video clip, keen applications, which have item removal, video purchasing, video clip healing and also fast perusing are performed with foundation demonstrating and also recovery summation. That is, both vital outline video reading and also explicit product recuperation reading can be performed on the very first video clip. Secondly, the techniques based on material matching for video access are evaluated. In view of these actualities, we provide a query by the criterion video clip recovery method, in this paper. We present a computation for comparability collaborating, to find the neighbourhood video clip set-ups with various sizes. In addition, the computation can find the similitude between an inquiry video clip and an item of another video clip group. Amid this treatment, a certainty price will be taken into consideration that makes the inexact similitude coordinating possible. The access methods based on info concept are additionally reviewed. In the recent years, a substantial arrangement of video documents is made and quickly sight, and also sound growth makes a brand-new examination aware preparing the world. Our proposed technique is in view of detailed theory. These structures consist of three essential components which integrate: shot border detection, ordered video clip synopsis, and submit and recover target video clip.


Video retrieval Object detection Content matching Video matching Video searching Information theory 


  1. 1.
    Zhang, X.G., Liang, L.H., Huang, Q., Liu, Y.Z., Huang, T.J., Gao, W.: An efficient coding scheme for surveillance videos captured by stationary cameras. Vis. Commun. Image Process. 77442A-1-10 (2010)Google Scholar
  2. 2.
    Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Trans 17(9), 1103–1120 (2007)Google Scholar
  3. 3.
    Dimitrova, N., Zhanhg, H.J., Shahrary, B., Sezan, I., Huang, T., Zakhor, A.: Applications of video-content analysis and retrieval. IEEE Multimedia 9(4) (2002)Google Scholar
  4. 4.
    Jiang, Y., Olson, I.R., Chun, M.M.: Organization of visual-short term memory. J. Exp. Psychol. Learn. Mem. Cogn. 26(3), 683–702 (2000)CrossRefGoogle Scholar
  5. 5.
    Antani, S., Kasturi, R., Jain, R.: A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern Recogn. 35, 945–965 (2002)CrossRefGoogle Scholar
  6. 6.
    Mandal, M.K., Idris, F., Panchanathan, S.: A critical evaluation of image and video indexing techniques in the compressed domain. Image Vis. Comput. 17, 513–529 (1999)CrossRefGoogle Scholar
  7. 7.
    Pentland, A., Picard, R., Sclaroff, S.: Photobook: tools for content-based manipulation of image database. In: SPIE Paper 2185-05, Storage and Retrieval of Image and Video Databases, San Jose, CA, pp. 34–47 (1994)Google Scholar
  8. 8.
    Ogle, V.E., Sonebraker, M.: Chatbot: retrieval from a relational database of images. IEEE Comput. 28(9) (1995)Google Scholar
  9. 9.
    Chang, S., Smith, J., Meng, J.: Efficient techniques for feature-based image video access and manipulation. In: Proceedings of 33rd Annual Clinic on Library Applications of Data Processing, Image Access and Retrieval (Invited Paper) (1996, March)Google Scholar
  10. 10.
    Cascia, M., Ardizzone, E.: JACOB: just a content-based query system for video databases. In: Proceedings of ICASSP-96, Atlanta Georgia, pp. 7–10 (1996)Google Scholar
  11. 11.
    Jiang, N.A., Merler, W., Smith, M., Tesic, J.R., Xie, J., Yan, L.: IBM research TRECVID-2008 video retrieval system. In: Proceedings of TREC Video Retrieval Workshop, Gaithersburg, MD, (2008, November)Google Scholar
  12. 12.
    Cmekova, Z., Pittas, I., Nikou, C.: Information theory-based shot cut/fade detection and video summarization. IEEE Trans. Circuit Syst. Video Technol. 16(1) (2006)Google Scholar
  13. 13.
    Pritch, Y., Rav-Acha, A., Peleg, S.: Nonchronological video synopsis and indexing. Pattern Anal. Mach. Intell. IEEE Trans. 30(11), 1971–1984 (2008)CrossRefGoogle Scholar
  14. 14.
    Liao, S., Zhao, G., Kellokumpu, V., Pietikäinen, M.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings Computer Vision and Pattern Recognition, 13–18 June 2010Google Scholar
  15. 15.
    Zhang, T., Lu, H., Li, S.Z.: Learning semantic scene models by object classification and trajectory clustering. IEEE Comput. Soc. (2009)Google Scholar
  16. 16.
    Hsieh, J.W., Yu, S.-L., Chen, Y.-S.: Motion-based video retrieval by trajectory matching. IEEE Trans. Circuits Syst. Video Technol. 16(3), 396–409 (2006)CrossRefGoogle Scholar
  17. 17.
    Wu, P., Manjunath, B.S., Newsam, S.D., Shin, H.D.: A texture descriptor for image retrieval and browsing. Content-based access of image and video libraries. In: Proceedings of IEEE Workshop on Computer Vision and Pattern Recognition, pp. 3–7 (1999)Google Scholar
  18. 18.
    Wang, S., Yang, J., Stan, Z., Li, S.N., et al.: A surveillance video analysis and storage scheme for scalable synopsis browsing. In: IEEE International Conference on Computer Vision Workshops (2011)Google Scholar
  19. 19.
    Bhat, D.N., Nayar, S.K.: Ordinal measures for image correspondence. IEEE Trans Pattern Anal. Mach. Intell. 20(4) (1998)Google Scholar
  20. 20.
    Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of EngineeringLincoln University CollegeKuala LumpurMalaysia

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