CFP last date
20 March 2024
Reseach Article

Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

by Aman Chadha, Sushmit Mallik, Ravdeep Johar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 20
Year of Publication: 2012
Authors: Aman Chadha, Sushmit Mallik, Ravdeep Johar
10.5120/8320-1959

Aman Chadha, Sushmit Mallik, Ravdeep Johar . Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval. International Journal of Computer Applications. 52, 20 ( August 2012), 35-42. DOI=10.5120/8320-1959

@article{ 10.5120/8320-1959,
author = { Aman Chadha, Sushmit Mallik, Ravdeep Johar },
title = { Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 20 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number20/8320-1959/ },
doi = { 10.5120/8320-1959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:47.555615+05:30
%A Aman Chadha
%A Sushmit Mallik
%A Ravdeep Johar
%T Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 20
%P 35-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz. , Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.

References
  1. A. D. Bimbo and P. Pala, "Visual Image Retrieval by Elastic Matching of User Sketches", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, issue 2, 121-132, February 1997.
  2. A. Pentland, R. Picard, and S. Sclaroff, "Photobook: Content-based manipulation of image databases. " International Journal of Computer Vision (IJCV), Vol. 18, No. 3, 233-254, June 1996.
  3. M. G. Christel and R. M. Conescu, "Addressing the challenge of visual information access from digital image and video libraries", Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries - JCDL '05, 69-73, 2005.
  4. M. N. Do and M. Vetterli, "Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback–Leibler Distance", IEEE Transactions on Image Processing, Vol. 11, No. 2, 146-158, February 2002.
  5. P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, "The FERET database and evaluation procedure for face recognition algorithms," Image and Vision Computing J. , Vol. 16, No. 5, 295-306, 1998.
  6. J. W. Bala,"Combining Structural and Statistical Features in a Machine Learning Technique for Texture Classification", IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems, Vol. 1, 175-183, 1990.
  7. Y. Liu, D. Zhangt, G. Lu, W. Y. Ma, "Study on Texture Feature Extraction in Region-Based Image Retrieval System", Multi-Media Modelling Conference Proceedings, 12th International, 8-15, 2006.
  8. D. A. Kumar and J. Esther, "Comparative Study on CBIR based by Color Histogram, Gabor and Wavelet Transform", Vol. 17, No. 3, 37-44, March 2011.
  9. S. Deb and Y. Zhang, "An Overview of Content-Based Image Retrieval Techniques", Proc. IEEE Int. Conf. on Advanced Information Networking and Application, Vol. 1, 59-64, 2004.
  10. J. Li, J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, 1075-1088, 2003.
  11. J. Z. Wang, J. Li, G. Wiederhold, "SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 23, No. 9, 947-963, 2001.
  12. Y. Rui, T. Huang, and S. Mehrotra, "Content-Based Image Retrieval with Relevance Feedback in MARS," Proc. IEEE Int'l Conf. Image Processing, 815-818, Oct. 1997
  13. Y. Rui, T. Huang, M. Ortega, and S. Mehrotra, "Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval," IEEE Trans. Circuits and Systems for Video Technology, Vol. 8, No. 5, 644-655, Sept. 1998.
  14. D. Harman, "Relevance Feedback Revisited," Proc. 15th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, 1-10, 1992.
  15. D. H. Kim and C. W. Chung, "Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval," Proc. ACM SIGMOD, 599-610, 2003.
  16. J. H. Su, W. J. Huang, P. S. Yu and V. S. Tseng "Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns", IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 3, 360-372, March 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Image Similarities Feature Matching Image Retrieval