CFP last date
22 April 2024
Reseach Article

Efficient Keyword-Based Image Search on the Internet using Concept Ontology

Published on March 2012 by Dilipkumar A. Borikar
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 6
March 2012
Authors: Dilipkumar A. Borikar

Dilipkumar A. Borikar . Efficient Keyword-Based Image Search on the Internet using Concept Ontology. International Conference in Computational Intelligence. ICCIA, 6 (March 2012), 11-15.

author = { Dilipkumar A. Borikar },
title = { Efficient Keyword-Based Image Search on the Internet using Concept Ontology },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 6 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 11-15 },
numpages = 5,
url = { /proceedings/iccia/number6/5131-1043/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Dilipkumar A. Borikar
%T Efficient Keyword-Based Image Search on the Internet using Concept Ontology
%J International Conference in Computational Intelligence
%@ 0975-8887
%N 6
%P 11-15
%D 2012
%I International Journal of Computer Applications

The tremendous growth of the World Wide Web has led to a considerable amount of information flooding in recent years. A huge volume of multimedia information that includes text, audio, video and image data, is being generated continuously, requiring the multimedia web databases to store them. This enormous data volume also necessitates efficient indexing mechanisms to facilitate faster retrieval. Multimedia systems and content-based image retrieval (CBIR) go hand in hand and they together have become one of the most challenging fields of research. CBIR addresses the problem of retrieval of relevant images from voluminous multimedia repositories using low-level image features. This work is aimed at retrieval of multimedia (specifically images) from the web. The CBIR approach is primarily an attribute-based representation of the images integrated with text-based retrieval. Web-pages containing the concept images are retrieved using the TF-IDF relationships. Concept ontology has been developed for augmenting the retrieval process. The proposed a keyword-based approach to image retrieval that uses the concept ontology information for intelligent retrieval eliminates manual annotation of images (web pages) by using ontology vocabulary in automated text extraction. Ontology serves as a means for providing semantic information about the objects in the domain of interest. Initial experimentation with the prototype system has lead to more precise search with a better average retrieval time. Use of concept ontology involving a document centered approach to image search yielded promising results.

  1. Yixin Chen, James Z. Wang and Robert Krovetz, 2003. “Content-based image retrieval by clustering,” Proc. 5th ACM SIGMM international workshop on Multimedia Information Retrieval (MIR 03), November 2003, pp: 253-262, ISBN: 1-58113-778-8, DOI:10.1145/973264.973295.
  2. Ritendra Datta, Jia Li and James Z. Wang, 2005. “Content-based image retrieval: approaches and trends of the new age,” Proc.7th ACM SIGMM international workshop on Multimedia Information Retrieval (MIR 05), November 2005, pp: 253-262, DOI:10.1145/1101826.1101866.
  3. Myron Flickner, Harpreet Sawhney, Wayne Niblack, Jonathan Ashley, Qian Huang, Byron Dom, Monika Gorkani, Jim Hafner, Denis Lee, Dragutin Petkovic, David Steele and Peter Yanker, 1995. "Query by Image and Video Content: The QBIC System," Computer, vol. 28, no. 9, September 1995, pp: 23-32, DOI:10.1109/2.410146.
  4. Q. Iqbal and J. K. Aggarwal, 2002. “Combining structure, color and texture for image retrieval: A performance evaluation,” Proc. 16th International Conference on Pattern Recognition (ICPR 02), vol. 2, IEEE, December 2002, pp: 438-443, DOI:10.1109/ICPR.2002.104833.
  5. M. L. Kherfi, D. Ziou and A. Bernardi, 2004. “Image retrieval from the World Wide Web: Issues, techniques and systems,” ACM Computing Surveys (CSUR), vol. 36, no. 1, March 2004, pp: 35-67, DOI:10.1145/1013208.1013210
  6. Jacob Köhler, Stephan Philippi, Michael Specht and Alexander Rüegg, 2006. “Ontology based text indexing and querying for the semantic web,” Knowledge-Based Systems, vol. 19, no. 8, Elsevier Science, December 2006, pp: 744-754, DOI:10.1016/j.knosys.2006.04.015.
  7. Yi-Chun Liao, 2008. “A weight-based approach to information retrieval and relevance feedback,” An International Journal of Expert Systems with Applications, vol. 35, no. 1-2, July 2008, pp: 254-261, DOI:10.1016/j.eswa.2007.06.032.
  8. Ying Liu, Dengsheng Zhang, Guojun Lu and Wei-Ying Ma, 2007. “A survey of content-based image retrieval with high-level semantics,” Pattern Recognition, vol.40, no. 1, January 2007, pp: 262-282, DOI:10.1016/j.patcog.2006.04.045.
  9. M. Mitra and B. B. Choudhuri, 2000. “Information retrieval from documents: A survey,” Information Retrieval, vol. 2, no. 2-3, May 2000, pp: 141-163, DOI: 10.1023/A: 1009950525500.
  10. Gerard Salton and Christopher Buckley, 1988. “Term-weighting approaches in automatic text retrieval,” Information Processing & Management, Elsevier Science, vol. 24, no. 5, January 1988, pp: 513-523, DOI: 10.1016/0306-4573(88)90021-0.
  11. T. Schreiber, B. Dubbeldam, J. Wielemaker and B. Wielinga, 2001. “Ontology based photo annotation,” IEEE Intelligent Systems, vol. 16, no. 3, May-June 2001, pp: 66-74, DOI:10.1109/5254.940028.
  12. Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta and Ramesh Jain, 2000. “Content-based image retrieval at the end of the early years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, December 2000, pp: 1349-1380, DOI:10.1109/34.895972.
  13. John R. Smith and Shih-Fu Chang, 1997. “Querying by color region using the VisualSEEK content-based visual query system,” Intelligent Multimedia Information Retrieval, MIT Press, 1997, pp: 23-41, ISBN: 0-262-63179-2.
Index Terms

Computer Science
Information Sciences


CBIR concept ontology semantic gap Image Content multimedia retrieval TF-IDF keyword-based image search