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Reseach Article

Enhance the Technique of Relevance Feedback for Content-based Multimedia Retrieval by using Mining Algorithm

by Aasma S. Mujawar, Kosbatwar Shyam P.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 24
Year of Publication: 2013
Authors: Aasma S. Mujawar, Kosbatwar Shyam P.
10.5120/11735-7363

Aasma S. Mujawar, Kosbatwar Shyam P. . Enhance the Technique of Relevance Feedback for Content-based Multimedia Retrieval by using Mining Algorithm. International Journal of Computer Applications. 67, 24 ( April 2013), 13-16. DOI=10.5120/11735-7363

@article{ 10.5120/11735-7363,
author = { Aasma S. Mujawar, Kosbatwar Shyam P. },
title = { Enhance the Technique of Relevance Feedback for Content-based Multimedia Retrieval by using Mining Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 24 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number24/11735-7363/ },
doi = { 10.5120/11735-7363 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:26:19.710592+05:30
%A Aasma S. Mujawar
%A Kosbatwar Shyam P.
%T Enhance the Technique of Relevance Feedback for Content-based Multimedia Retrieval by using Mining Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 24
%P 13-16
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today images, multimedia are immensely important in information retrieval system. In existing relevance feedback technique , there is semantic gap between high level concepts and low level features of images as well as videos, another drawback is according to user requirement we cannot retrieve relevant multimedia data (images, videos) from multimedia database and image database. To overcome from this drawback in Content based Multimedia Retrieval (CBMR), using navigation pattern relevance feedback technique to retrieve most relevant videos, images from multimedia data according to user requirement. To provide efficient and effective retrieval of content based multimedia data and images from multimedia database like video data, images by using relevance feedback technique and mining algorithm.

References
  1. Ja-Hwung Su, Philip S. Yu, Wei-Jyun Huang ,Efficient Relevance Feedback for CBIR by Mining User Navigation Patterns IEEETrans On Knowledge And Data Engineering VOL. 23,No March 2011.
  2. Peng-Yeng Yin ,Bir Bhaneu Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning IEEETrans on Pattern analysis and Machine Intellgence, vol. 27. No 10, October 2005.
  3. K. Porkaew, K. Chakrabarti, and S. Mehrotra, Query Refinement for Multimedia Similarity Retrieval in MARS, Proc. ACM Int'l Multimedia Conf. (ACMMM), pp. 235-238, 1999.
  4. Mianchu Chen,Ping Fu,Yuan sun,Hui zhang Image Retrieval Based on Multi-feature similarity score fusion using Genetic Algorithm The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010.
  5. P. Y. Yin, B. Bhanu, K. C. Chang, and A. Dong, Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no 10, pp. 1536-1551, Oct. 2005.
  6. X. S. Zhou and T. S. Huang, Relevance Feedback for Image Retrieval: Comprehensive Review, Multimedia Systems, vol. 8,no. 6, pp. 536-544, Apr. 2003.
  7. R. Fagin, Combining Fuzzy Information from Multiple Systems, Proc. Symp. Principles of Database Systems (PODS), pp. 216- 226, June1996. .
  8. R. Fagin, Fuzzy Queries in Multimedia Database Systems, Proc. Symp. Principles of Database Systems (PODS), pp. 1-10, June 1998.
  9. Mianchu Chen,Ping Fu,Yuan sun,Hui zhang Image Retrieval Based on Multi-feature similarity score fusion using Genetic Algorithm The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010.
  10. K. Vu, K. A. Hua, and N. Jiang, Improving Image Retrieval Effectiveness in Query-by-Example Environment, Proc. 2003 ACM Symp. Applied Computing, pp. 774-781, 2003.
  11. A. Pentalnd, R. W. Picard, and S. Sclaroff, Photobook: Content-Based Manipulation of Image Databases Int'l J. Computer Vision(IJCV), vol. 18, no. 3, pp. 233-254, June 1996.
  12. J. J. Rocchio, Relevance Feedback in Information Retrieval. The SMART Retrieval System—Experiments in Automatic Document Processing, pp. 313-323, Prentice Hall, 1971.
Index Terms

Computer Science
Information Sciences

Keywords

Content-based Multimedia Retrieval Relevance Feedback Query Image Reweighting Query Expansion Query Point Movement