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Reseach Article

Content based Image Retrival

Published on None 2011 by S.Dharani, Dr.B.Justus Rabi, Dr.A.N.Nanda Kumar, J.Patrick Joe Carmel
International Conference on Emerging Technology Trends
Foundation of Computer Science USA
ICETT2011 - Number 3
None 2011
Authors: S.Dharani, Dr.B.Justus Rabi, Dr.A.N.Nanda Kumar, J.Patrick Joe Carmel
4c5792f0-836d-4b5a-92c5-2a6b25b2ae28

S.Dharani, Dr.B.Justus Rabi, Dr.A.N.Nanda Kumar, J.Patrick Joe Carmel . Content based Image Retrival. International Conference on Emerging Technology Trends. ICETT2011, 3 (None 2011), 32-37.

@article{
author = { S.Dharani, Dr.B.Justus Rabi, Dr.A.N.Nanda Kumar, J.Patrick Joe Carmel },
title = { Content based Image Retrival },
journal = { International Conference on Emerging Technology Trends },
issue_date = { None 2011 },
volume = { ICETT2011 },
number = { 3 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 32-37 },
numpages = 6,
url = { /proceedings/icett2011/number3/3509-icett019/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emerging Technology Trends
%A S.Dharani
%A Dr.B.Justus Rabi
%A Dr.A.N.Nanda Kumar
%A J.Patrick Joe Carmel
%T Content based Image Retrival
%J International Conference on Emerging Technology Trends
%@ 0975-8887
%V ICETT2011
%N 3
%P 32-37
%D 2011
%I International Journal of Computer Applications
Abstract

In this paper, we propose a content-based image retrieval system which takes into account the local attributes of the image for defining the feature space. This method presents a way to localize the characteristics of the queries by partitioning the image into a rectangular grid and applying a different feature vector to each region in the similar measuring phase. The assignment map specifying the feature space for each image region is implicitly selected by the user, through the system interface, according to perception of the content. This is the most important aspect of the system, which provides flexibility to the user to query at the object level by selecting the “type” of the regions. User only intervention to the system is at this phase, but the way of preparing the query, directs the system’s similarity calculation in the later stages. The experiments indicate that the proposed system yields better results for images having distinctive objects compared to global systems using the same features for the entire image.

References
  1. D. A. Forsyth, J. Ponce, Computer Vision: A modern Approach, Chapter 25, Prentice-Hall, 2001.
  2. Y. Rui, T.S. Huang, “Image Retrieval: Current Techniques, Promising Directions and Open Issues”, Journal of Visual Communication and Image Representation, vol. 10, pp. 1–23, 1999.
  3. A. A. Goodrum, “Image Information Retrieval: An Overview of Current Research”, Special Issue on Information Science Research, vol. 3, no. 2, 2000.
  4. C. Carson, S. Belongie, H. Greenspan, J. Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying”, IEEE Trans PAMI, vol. 24, no. 8, 2002.
  5. Z. Stejic, Y. Takama, K. Hirota, “Genetic Algorithm-based Relevance Feedback for Image Retrieval Using Local Similarity Patterns”, Information Processing & Management, 2002.
  6. X. S. Zhou, T. S. Huang, “Relevance Feedback in Image Retrieval: A Comprehensive Review”, CVPR 2001 CBAIVL Workshop.
  7. J. Z. Wang, J. Li, G. Wiederhold, “Simplicity: Semantics-sensitive Integrated Matching for Picture Libraries,” IEEE Trans. PAMI, vol 23, no.9, 2001.
  8. MPEG-7 Home page, http://www.darmstadt.gmd.de/mobile/MPEG7/index.html.
  9. MPEG 7 Experimentation Model Software, http://www.lis.ei.tum.de/research/bv/topics/mmdb/e_mpeg7.html
  10. Marinovic,I.; FurstnerI.; Manufaktura d.o.o., Subotica , “Content-based image retrieval” Intelligent Systems and Informatics, 2008. SISY 2008. 6th International Symposium on Sept. 2008.pp 1-6.
Index Terms

Computer Science
Information Sciences

Keywords

DataBase Generation Subsystem DataBase Retrieval Subsystem Feature Matching Subsystem