A Survey on Various Approaches for Multimedia Search Engines

Authors

  • Kamatkar Vinalini Vinayak  M.Tech Scholar, Department of Computer Science and Engineering Tulsiramji Gaikwad-Patil College of Engineering and Technology Nagpur, Maharashtra, India
  • Prof. Jayant Adhikari  Department of Computer Science and Engineering Tulsiramji Gaikwad-Patil College of Engineering and Technology Nagpur, Maharashtra, India
  • Prof. Rajesh Babu  Department of Computer Science and Engineering Tulsiramji Gaikwad-Patil College of Engineering and Technology Nagpur, Maharashtra, India

Keywords:

Re-Ranking, Multimedia Retrieval, Audio-Video Feature Extraction

Abstract

Now a days searching of images and video on internet are very popular, but most of the times searching result not exactly matches with the searched key. Re-ranking, as an effective way to improve the results of web based multimedia search. This is adopted by commercial search engines such as Google. The proposed re-ranking approach is capable to work with all multimedia types: video, image, and audio. The search engines are mostly based on text and constrained as the user search by keyword which results into uncertainty among multimedia. Due to which noisy or irrelevant images or video are present as retrieved results. The purpose of multimedia search re-ranking is to reorder retrieved elements to get optimal rank list. So for that group of descriptors are used with weight and weight are assigned to it dynamically for getting accurate multimedia files. In this paper we discuss different methods for web multimedia re-ranking and propose new re-ranking technique to acquire the accurate query result and result shows that it retrieves most relevant files to the top.

References

  1. Zhu, Y., Xiong, N., Park, J., and He, R., “A Web Image Retrieval Reranking Scheme with Cross-Modal Association Rules”, InternationalSymposium on Ubiquitous Multimedia Computing, Issue 13, Pages 83 - 86, 2008.
  2. Chen, S., Wang, F., Song, Y., and Zhang, C., “Semi-supervised ranking aggregation”, Information Processing & Management, Volume 47, Issue 3, Pages 415-425, 2011.
  3. Singhal, R., & Srivastava, S. R. , "Enhancing the page ranking for search engine optimization based on weightage of in-linked web pages." Recent Advances and Innovations in Engineering (ICRAIE), 2016 International Conference on. IEEE, 2016.
  4. Goodrum, A. and A. Spink (1999), “Visual Information Seeking: A Study of Image Queries on the World Wide Web,” In Proceedings the 1999 Annual Meeting of the American Society for Information Science, Washington, DC, November 1999, pp. 665–674
  5. Lu, M., Huang, Y., Xie, M., and Liu, J., “Rank hash similarity for fast similarity search”, Information Processing & Management, Volume 49,Issue 1, Pages 158-168,2013.
  6. J.Cui, F. Wen, et.al, “Real time Google and live image search reranking”, The 16th ACM international conference on Multimedia, Pages 729-732, 2008.
  7. X. Tang, K.Liu, J. Cui, et.a, “IntentSearch: Capturing User Intention for One-Click Internet Image Search”, IEEE Transactions On Pattern Analysis and Machine Intelligence Vol. 34, No.7 pages 1342 – 1353, July 2012.
  8. Y. Rui, T. S. Huang, M. Ortega, et.al, “Relevance feedback: a power tool for interactive Content-based image retrieval”, IEEE Transactions On Circuits and Systems for Video Technology, 1998.
  9. W. Y. Ma and B. S. Manjunath, “A toolbox for navigating large image databases, multimedia system,” 3(7), 1999, 184-198.
  10. R. Yan, E. Hauptmann, and R. Jin, “Multimedia Search with Pseudo-Relevance Feedback,” in Proc. Int. Conf. Image and Video Retrieval, 2003.
  11. J. Cui, F. Wen, and X. Tang, “Real Time Google and Live Image Search Re-Ranking,” in Proc. 16th ACM Int. Conf. Multimedia, 2008.
  12. J. Cai, Z. Zha, W. Zhou, and Q. Tian, “Attribute-Assisted Reranking for Web Image Retrieval,” in Proc. 20th ACM Int. Conf. Multimedia, 2012.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-03-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kamatkar Vinalini Vinayak, Prof. Jayant Adhikari, Prof. Rajesh Babu, " A Survey on Various Approaches for Multimedia Search Engines" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.01-06, March-April-2019.