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CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding

by Vandana Vinayak, Sonika Jindal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 9
Year of Publication: 2017
Authors: Vandana Vinayak, Sonika Jindal
10.5120/ijca2017913282

Vandana Vinayak, Sonika Jindal . CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding. International Journal of Computer Applications. 161, 9 ( Mar 2017), 1-7. DOI=10.5120/ijca2017913282

@article{ 10.5120/ijca2017913282,
author = { Vandana Vinayak, Sonika Jindal },
title = { CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 9 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number9/27173-2017913282/ },
doi = { 10.5120/ijca2017913282 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:06:57.121445+05:30
%A Vandana Vinayak
%A Sonika Jindal
%T CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 9
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In content-based Image Retrieval (CBIR) application, a large amount of data is processed. Among various low-level features like color, shape and texture, color is an important feature and represented in the form of histogram. It is essential that features required to be coded in such a way that the storage space requirement is low and processing speed is high. In this paper, we propose a method for indexing of images in the large database with lossy compression technique known as Block Truncation Coding (BTC) along with two different color feature extraction methods - Color Moment and Color Auto-correlogram. Block truncation coding divided the original image into multiple non-overlapping blocks and then retrieve the required features. The proposed method performs better.

References
  1. Meenakshi Garg Amit Singla. Cbir approach based on combined hsv, auto correlogram, color moments and gabor wavelet. volume 3, pages 9007–9012. International Journal Of Engineering And Computer Science, ISSN:2319-7242, 10 October 2014.
  2. Martha Saenz Edward J. Delp and Paul Salama. Video and Image Processing Laboratory, chapter 5.
  3. Dipankar Hazra. Retrieval of color image using color correlogram and wavelet filters. Proc. of International Conference on Advances in Computer Engineering, 2011.
  4. Hong-Jiang Zhang Jufu Feng Hui Yu, Mingjing Li. Color texture moments for content-based image retrieval. pages 929– 932, September 2002.
  5. B.S. Manjunath and W.Y. Ma. Texture features for browsing and retrieval of image data. volume 18, pages 837–842, 1996.
  6. W. Niblack. ”the qbic project: Querying images by content using color, texture and shape. volume 1908, pages 173–187, 1993.
  7. Dr.Bob Fisher Noah Keen. Color Moment. 2005.
  8. V.E. Ogle and M. Stonebraker. Retrieval from a relational database of images. pages 40–48, 1995.
  9. Olli Nevalainen Pasi Franti and Timo Kaukoranta. Compression of digital images by block truncation coding:a survey. Number 37(4), pages 308–332. The Computer Journal, 1994.
  10. Richard E. Woods Rafael C. Gonzalez. Digital Image Processing. Pearson Education, third edition, 2014.
  11. M. Choras R.S. Choras, T. Andrysiak. Integrated color, texture and shape information for content-based image retrieval. 10, pages 333–343, 2007.
  12. K. Hemachandran S. Mangijao Singh. Content-based image retrieval using color moment and gabor texture feature. volume 9 of Issue 5. IJCSI International Journal of Computer Science Issues ISSN (Online): 1694-0814, September 2012.
  13. Vadivel A Shaila S G. Block encoding of color histogram for content based image retrieval applications. volume 6, pages 526–533, 2012.
  14. J.L. Shih and L.H. Chen. Color image retrieval based on primitives of color moments. IEEE Proceedings online no. 20020614, 2002.
  15. M. Stricker and M. Orengo. Similarity of color images. volume 2420, pages 381–392. SPIE Conference on Storage and Retrieval for Image and Video Databases, 1995.
  16. M.Z. Swain and D.H. Ballard. Color indexing. volume 7 (1), pages 11–32. International Journal of Computer Vision, 1991.
  17. B. Xue and L. Wanjun. Research of image retrieval based on color. IEEE International Forum on Computer Science Technology and Applications, 2009.
  18. W.W.Y. Ng D.S. Yeung Z.C. Huang, P.P.K. Chan. Contentbased image retrieval using color moment and gabor texture feature. pages 719–724. Poceedings of the IEEE Ninth International Conference on Machine Learning and Cybernetics, Qingdao,, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR Color Moment Color Auto-Correlogram Block Truncation Coding

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