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Reseach Article

Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features

by Pushpalatha K. R., Asha Gowda Karegowda, D. Ramesh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 6
Year of Publication: 2017
Authors: Pushpalatha K. R., Asha Gowda Karegowda, D. Ramesh
10.5120/ijca2017914328

Pushpalatha K. R., Asha Gowda Karegowda, D. Ramesh . Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features. International Journal of Computer Applications. 167, 6 ( Jun 2017), 30-36. DOI=10.5120/ijca2017914328

@article{ 10.5120/ijca2017914328,
author = { Pushpalatha K. R., Asha Gowda Karegowda, D. Ramesh },
title = { Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 6 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 30-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number6/27778-2017914328/ },
doi = { 10.5120/ijca2017914328 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:54.087107+05:30
%A Pushpalatha K. R.
%A Asha Gowda Karegowda
%A D. Ramesh
%T Identification of Similar Looking Bulk Split Grams using GLCM and CGLCM Texture Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 6
%P 30-36
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content based image retrieval (CBIR) is an automated way to retrieve images based on the visual content or image features itself. Visual inspection of food type is tiresome and time consuming task. This paper presents the retrieval of similar looking bulk split gram images using Grey Level Co-occurrence Matrix (GLCM) and Color Grey Level Co-occurrence Matrix (CGLCM) texture features. Texture feature matching procedure is based on three distance measures namely, Euclidean distance, Canberra distance and City block distance. The performance of a retrieved image is measured in terms of Precision. Experimental results show that the CGLCM provides better retrieving result than GLCM.

References
  1. Asha Gowda Karegowda, Bharathi, “Enhancing CBIR Performance using Evolutionary Algorithms Assisted Significant Feature Selection”, A Filter Approach International Journal of Applied Research on Information Technology and Computing,Vol. 7,No.1,Page:53-59, April 2016.
  2. Asha Gowda Karegowda, Pushpalatha.K.R, Chaitra.M,” Color Histogram Based Image Retrieval –A survey”, International Journal of Advanced Research in computer science, Vol 4, Page: 119-126, Nov-Dec 2013.
  3. N.S. Visen, J. Paliwal, D.S. Jayas,N.D.G. White, “Image analysis of bulk grain samples using neural networks”, Journal of Canadian Biosystems Engineering, Vol. 46, Page:7.11-7.15, 2004.
  4. Basavaraj .S. Anami, Vishwanath.C.Burkpalli, “Texture based Identification and Classification of Bulk Sugary Food Objects”, International Conference on Computer Science and Engineering (ICGST), Vol.9, Issue 4, Page: 9-14, 2009.
  5. Basavaraj S. Anami Naveen N. M.2 ,N. G. Hanamaratti ,”Behavior of HSI ColorCo-Occurrence Features in Variety Recognition from Bulk Paddy Grain Image Samples”, International Journal of Signal Processing and Pattern Recognition,Vol. 8, No. 4, Page:19-30, 2015.
  6. Basavaraj S. Anami, Vishwanath C burkpalli, “Color Based Recognition and Estimation of Temperature Levels of Images of Boiled Food Grains”, International Journal of Computer Applications, Vol.1 No.14,Page:98-103, 2010.
  7. Neelamma K. Patil Ravi M. Yadahalli Jagadeesh Pujari, “Comparison between HSV and YCbCr Color Model Color-Texture based Classification of the Food Grains”, International Journal of Computer Applications, Vol.34, No.4, Page: 51-57, 2011.
  8. Neelamma K. Patil, Virendra S. Malemath, Ravi M. Yadahalli, “Color and Texture Based Identification and Classification of food Grains using different Color Models and Haralick features”, International Journal on Computer Science and Engieering (IJCSE),Vol.3, No.12,Page:3669-3680,Dec 2011.
  9. Neelamma K. Patil, Ravi M. Yadahalli, Jagadeesh Pujari, ”The Effect of Block Size, Training set and K-value in the Classification of Food Grains using L*a*b color model by combining Color and Texture Information”, Global Journal of Computer Application and Technology, Vol.2, No.1, Page: 884-889, 2012.
  10. Basvaraj .S. Anami, Jagadeesh D.Pujari,Rajesh Yakkundimath,”Identification and Classification of Normal and Affected Agriculture/horticulture Produce Based on Combined Color and Texture Feature Extraction”, International Journal of Computer Applications in Engineering Sciences, Vol.1, Issue 3, Page:356-360, 2011.
  11. Dayanand Savakar,”Recognition and Classification of Similar Looking Food Grain Images using Artificial Neural Networks”, Journal of Applied Computer Science and Mathematics, Vol.6, No. 13, Page: 61-65, 2012.
  12. Dayanand Savakar, “Identification and Classification of Bulk FruitsImages using Artificial Neural Networks”, International Journal of Engineering and Innovative Technology (IJEIT), Vol. 1, Issue 3, Page: 36-40, 2012.
  13. P.K. Mallick, S.Rout, “Identification and Classification of Similar Looking Food Grains”, International Journal on Advanced Computer Theory and Engineering (IJACTE), Vol. 1, Issue 2, Page: 139-144, 2013.
  14. K s h .Robert Singh, Saurabh Chaudhury, “An Efficient Technique for Rice Grain Classification using Back Propagation Neural Network and Wavelet Decomposition”, IET Computer Vision 2016.
  15. S. J. Mousavi Rad, F. Akhlaghian Tab, K. Mollazade, “Application of Imperialist Competitive Algorithm for Feature Selection: A Case Study on Bulk Rice Classification”, International Journal of Computer Applications, Vol.40, No.16, Page: 41-48, 2012.
  16. Jagadeesh. D. Pujari, Rajesh. Yakkundimath, A. S. Byadgi, “Reduced Color and Texture features based Identification and Classification of Affected and Normal fruits’ images”, International Journal of Agricultural and Food Science,Vol.3, No.3, Page:119-127, 2013.
  17. Jagadeesh D.Pujari,Rajesh Yakkundimath, Abdulmunaf S. Byadgi,”Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features”, International Journal of Computer Applications, Vol.93, No.11, Page:17-24, 2014.
  18. Majumdar S., Jayas D.S “Classification of bulk samples of cereal grains using machine vision”, Journal of Agricultural Engineering Research, Vol.73, Page:35-47, 1999.
  19. Iman Golpour, Jafar Amiri Parian ,Reza Amiri Chayjan, ”Identification and Classification of Bulk Paddy, Brown,and White Rice Cultivars with Colour Features Extraction using Image Analysis and Neural Network”, Czech Journal of Food Science,Vol.32, No.3, Page:280-287, 2014.
  20. Basavaraj S. Anami, Dayanand G. Savakar, “Suitability of Feature Extraction Methods in Recognition and Classification of Grains,Fruits and Flowers”, International Journal of Food Engineering, Vol. 7 , Issue 1, Art.9, 2011.
  21. Basavaraj Anami, Dayanand G. Savakar, ”Effect of Foreign Bodies on Recognition and Classification of Bulk Food GrainsImage Samples”, Journal of Applied Computer Science, Vol.3, No.6, Page:77-83, 2009.
  22. R.M. Haralick, K. Shanmugam, and I.H. Dinstein. “Textural features for image classification” IEEE Transactions on Systems, Man, and Cybernetics, Page: 237-247, 1973.
  23. Moulay A. Akhloufi, Xavier Maldague, Wael Ben Larbi,”A New Color-Texture Approach for Industrial Products Inspection”, Journal Of Multimedia, Vol. 3, No. 3, Page: 44-50, July 2008.
  24. Miroslav Benco, Robert HUDEC,”Novel Method for Color Textures Features Extraction Based on GLCM”, Radio Engineering, Vol. 16, No. 4, Page: 64-67, Dec 2007.
Index Terms

Computer Science
Information Sciences

Keywords

CBIR GLCM CGLCM Euclidean Distance Canberra Distance City Block Distance Precision.