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Reseach Article

An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture

by Mayank Jain, Divakar Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 7
Year of Publication: 2016
Authors: Mayank Jain, Divakar Singh
10.5120/ijca2016908889

Mayank Jain, Divakar Singh . An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture. International Journal of Computer Applications. 145, 7 ( Jul 2016), 6-11. DOI=10.5120/ijca2016908889

@article{ 10.5120/ijca2016908889,
author = { Mayank Jain, Divakar Singh },
title = { An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 7 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number7/25288-2016908889/ },
doi = { 10.5120/ijca2016908889 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:08.366981+05:30
%A Mayank Jain
%A Divakar Singh
%T An Efficient Technique for Image Retrieval from the Large Database on the Basis of Color and Texture
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 7
%P 6-11
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day’s development of multimedia technology, the possibilities of utility of large databases is rapidly increasing. To handle its management and retrieval CBIR is the best and effective method. CBIR technique uses the visual contents like as color, shape and texture that are called features, to searching, browsing, and navigation of query images for large image databases. Color is the visual perceptual property corresponding in humans to the categories called red, blue and yellow etc. Texture is the image and especially physical quality of a surface. Texture is the characteristic structure of the interwoven or intertwined outfit, strands or the like that make up a textile fabric. In this paper we present utility of CBIR system with color and texture features. And we design a color filter with the help of extract the red channel, green channel and blue channel from the original image. After it we find a texture of all channels with the help of statistical method. The combination of texture of red channel, green channel and blue channel we create a feature vector for all images of the database. Experimental results are shows the average accuracy, average precision rate and average retrieval rate. That is better than other existing method.

References
  1. R. Jain. “Visual Information Management Systems.” Proc. US NSF Workshop, ed., 1992.
  2. Arnold W.M. Smeulders, S, Marcel Worring, Simone Santini, Amarnath Gupta, and Ramesh Jain. 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.
  3. Mona Mahrous Mohammeda, Amr Badrb, M.B. Abdelhalima. Image classification and retrieval using optimized Pulse-Coupled Neural Network. doi:10.1016/j.eswa.2015.02.019.
  4. Sourabh Shrivastava, Satish Kumar Singh, Dhara Singh Hooda, “Statistical Texture and Normalized Discrete Cosine Transform based Automatic Soya Plant Foliar Infection Cataloguing,” British Journal of Mathematics and Computer Sciences, vol. 4, no. 20, pp. 2901-2916, 2014.
  5. Pedro H. Bugatti, Daniel S. Kaster, Marcelo Ponciano-Silva, Caetano Traina Jr., Paulo M. Azevedo-Marques, Agma J.M. Traina. PRoSPer: Perceptual similarity queries in medical CBIR systems through user profiles Original Research ArticleComputers in Biology and Medicine, Volume 45, 1 February 2014, Pages 8-19.
  6. Manisha Verma, Balasubramanian Raman, Subrahmanyam murala. “Local extrema co-occurrence pattern for color and texture image retrieval.” Volume 165, Pages 255–269, 1 October 2015.
  7. Meng Jian, Cheolkon Jung, Yanbo Shen, Juan Liu. “Interactive image retrieval using constraints.” Volume 161, Pages 210–219, 5 August 2015.
  8. Reshma Khemchandani, Pooja Saigal. “Color image classification and retrieval through ternary decision structure based multi-category TWSVM.” Volume 165, Pages 444–455, 1 October 2015.
  9. P. Vijaya Bhaskar reddy, A. Rama Mohan Reddy. “Content based image indexing and retrieval using directional local extrema and magnitude patterns.” Volume 68, Issue 7, Pages 637–643, July 2014.
  10. M. Yasmin M. Sharif. “An Efficient Content Based Image Retrieval using EI Classification and Color Features.” Volume 12, Issue 5, Pages 877–885, October 2014.
  11. Mohsen Sardari Zarchi, Amirhasan Monadjemi. “A semantic model for general purpose content-based image retrieval systems.” Volume 40, Issue 7, Pages 2062–2071, October 2014.
  12. Ghanshyam Raghuwanshi, Vipin Tyagi. “Texture image retrieval using adaptive tetrolet transforms.” Volume 132, Issue 4, Pages 50–57, January 2016.
  13. Santosh Kumar Vipparthi, Subrahmanyam Murala, Shyam Krishna Nagar. “Dual directional multi-motif XOR patterns: A new feature descriptor for image indexing and retrieval.” Volume 126, Issues 15–16, Pages 1467–1473, August 2015.
  14. Hong-Ying Yang, Na Xu, Wei-Yi Li. “Color image representation using invariant exponent moments “Volume 46, Pages 273–287, August 2015.
  15. Michael B. Martin and Amy E. Bell, “New Image Compression Techniques using Multi wavelets and Multi wavelet Packets”, IEEE Transactions on image processing, Vol. 10, No. 4, April 2001.
  16. Hiremath P. S, Shivashankar. S. “Wavelet based features for texture classification”, GVIP Journal, Vol.6, Issue 3, pp 55-58, December 2006.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR feature vector Euclidean distance precision and recall.