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Texture Features and Decision Trees based Vegetables Classification

Published on August 2012 by Suresha M, Sandeep Kumar K S, Shiva Kumar G
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National Conference on Advanced Computing and Communications 2012
Foundation of Computer Science USA
NCACC - Number 1
August 2012
Authors: Suresha M, Sandeep Kumar K S, Shiva Kumar G
d14be88b-e536-4588-8ed0-92ad1f523a38

Suresha M, Sandeep Kumar K S, Shiva Kumar G . Texture Features and Decision Trees based Vegetables Classification. National Conference on Advanced Computing and Communications 2012. NCACC, 1 (August 2012), 21-26.

@article{
author = { Suresha M, Sandeep Kumar K S, Shiva Kumar G },
title = { Texture Features and Decision Trees based Vegetables Classification },
journal = { National Conference on Advanced Computing and Communications 2012 },
issue_date = { August 2012 },
volume = { NCACC },
number = { 1 },
month = { August },
year = { 2012 },
issn = 0975-8887,
pages = { 21-26 },
numpages = 6,
url = { /proceedings/ncacc/number1/7992-1008/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advanced Computing and Communications 2012
%A Suresha M
%A Sandeep Kumar K S
%A Shiva Kumar G
%T Texture Features and Decision Trees based Vegetables Classification
%J National Conference on Advanced Computing and Communications 2012
%@ 0975-8887
%V NCACC
%N 1
%P 21-26
%D 2012
%I International Journal of Computer Applications
Abstract

The proposed work deals with an approach to perform texture extraction of vegetables images for classification. The work has been carried out using watershed for segmentation. The vegetables textures features like red component, green component, skewness, kurtosis, variance, and energy are extracted. The method has been employed to normalize vegetable images and hence eliminating the effects of orientation using image resize technique with proper scaling. Finally, Decision Tree classifier is applied to the above features which return the results of the classification.

References
  1. Andrew, B. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition. 30(7), 1145-1159.
  2. Benbrahim H, A comparative study of pruned decision trees and fuzzy decision trees, International Conference of the North American Fuzzy Information Processing Society, 227 – 231, 2000.
  3. Buzera M. , Groza V. , Prostean G. , Prostean O. , "Techniques of Analyzing the Color of Produces for Automatic Classification", Intelligent Engineering Systems, 2008. INES 2008. International Conference on, 209-214, 25-29 Feb. 2008.
  4. Cesar, F. Peter, F. and Jose, H. 2003. Improving the AUC of probabilistic estimation trees. Proc. of the 14th European Conf. on Mach. Learning. 121-132, Springer.
  5. Fernandez C. , Suardiaz J. , Jimenez C. , Navarro P. J. , Toledo A. , Iborra A. , "Automated visual inspection system for the classification of preserved vegetables", Industrial Electronics, 2002. ISIE 2002. Proceedings of the 2002 IEEE International Symposium on, 265–269, 2002.
  6. Foster, P. and Venkateswarlu, K. 1999. A survey of methods for scaling up inductive algorithms. Data Mining and Knowledge. 3(2), 131-169.
  7. Leo, Breiman,. Jerome, Friedman,. Charles, Stone. and Richard, Olshen. 1984. Classification and Regression Trees.
  8. Pla F. , Sanchiz J. M. , Sanchez J. S. , "An integral automation of industrial fruit and vegetable sorting by machine vision", Emerging Technologies and Factory Automation, 2001. Proceedings. 2001 8th IEEE International Conference on, 541 - 546 vol. 2, 15-18 Oct. 2001.
  9. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Prentice-Hall, 617-620, 2002.
  10. Ross, Quinlan. 1993. C4. 5: Programs for Machine Learning. Morgan Kaufmann. San Francisco, CA, USA.
  11. Singh M. , Singh P. , Hardeep Singh, "Decision Tree Classifier for Human Protein Function Prediction", Advanced Computing and Communications, 2006. ADCOM 2006. International Conference on, 564-568, 20-23 Dec. 2006.
  12. Thangaparvathi B. , Anandhavalli D. , Mercy Shalinie S. , "A high speed decision tree classifier algorithm for huge dataset", Recent Trends in Information Technology (ICRTIT), 2011 International Conference on, 695-700, 3-5 June 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Classifier Texture Features Vegetables Classification