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Information Retrieval Using Image Attribute Possessions

  • D. SaravananEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Extracting defined information from the huge data set really challenging task for many researchers, especially this data set like image data’s process is too complex. As image data consist of motion, time, text, audio, pixel difference and more. From this complex data set, extracting the domain knowledge takes more time. This process differs from traditional text mining, because the nature of the data sets. Extracting information from image data, user needs additional knowledge; i.e., users required domain knowledge. This attracts many users concentrate on this field. Currently, many research works carried on this particular domain. Advancement of technology more and more image data is created and uses, for this urgent attention required in the field of image mining. This paper focuses on image mining help of clustering technique. First video data are grouped into frames, from the cleaned frameset process are done client- and server-side operations. The proposed technique works well, and experimental results also verified this.

Keywords

Video data mining Key frame analysis Clustering technique Image mining Frame comparison Knowledge extraction 

References

  1. 1.
    Pan, J.-Y., Faloutsos, C.: VideoCube: a new tool for video mining and classification. In: ICADL, Dec 2002, SingaporeGoogle Scholar
  2. 2.
    Saravanan, D.: Design and implementation of feature matching procedure for video frame retrieval. Int. J. Control Theory Appl. 9(7), 3283–3293 (2016)Google Scholar
  3. 3.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advanced in Knowledge Discovery and Data Mining. AAAI/MIT press (1996)Google Scholar
  4. 4.
    Ng, R.T., Han, J.: CLARANS: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5) (2002)CrossRefGoogle Scholar
  5. 5.
    Zhao, W., Wang, J., Bhat, D., Sakiewicz, K., Nandhakumar, N., Chang, W.: Improving color based video shot detection. In: IEEE International Conference on Multimedia Computing and Systems, vol. 2, pp. 752–756 (1999)Google Scholar
  6. 6.
    Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying scene breaks. In: Proceedings of the ACM Multimedia 95, San Francisco, CA, pp. 189–200, November 1995Google Scholar
  7. 7.
    Saravanan, D.: Image frame mining using indexing technique. In: Data Engineering and Intelligent Computing, pp. 127–137. Springer (2017). ISBN:978-981-10-3223-3Google Scholar
  8. 8.
    Saravanan, D.: Video content retrieval using image feature selection. Pak. J. Biotechnol. 13(3), 215–219 (2016)Google Scholar
  9. 9.
    Shyu, M., Xie, Z., Chen, M., Chen, S.: Video semantic event/concept detection using a subspace-based multimedia data mining framework. IEEE Trans. Multimed. 10, 252–259 (2008)CrossRefGoogle Scholar
  10. 10.
    Saravanan, D.: Information retrieval using: hierarchical clustering algorithm. Int. J. Pharm. Technol. 8(4), 22793–22803Google Scholar
  11. 11.
    Saravanan, D.: Effective video data retrieval using image key frame selection. Adv. Intell. Syst. Comput. 145–155Google Scholar
  12. 12.
    Zhang, L., Lin, F., Zhang, B.: A CBIR method based on color-spatial feature. In: The IEEE Region 10 Conference Proceedings, pp. 166–169, Sept 1999Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Operations & ITICFAI Business School (IBS)HyderabadIndia
  2. 2.The ICFAI Foundation for Higher Education (IFHE) (Deemed-to-be-University u/s 3 of the UGC Act 1956)HyderabadIndia

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