CFP last date
20 December 2024
Reseach Article

Intra-class Recognition of Fruits using Color and Texture Features with Neural Classifiers

by Susovan Jana, Ranjan Parekh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 11
Year of Publication: 2016
Authors: Susovan Jana, Ranjan Parekh
10.5120/ijca2016911283

Susovan Jana, Ranjan Parekh . Intra-class Recognition of Fruits using Color and Texture Features with Neural Classifiers. International Journal of Computer Applications. 148, 11 ( Aug 2016), 1-6. DOI=10.5120/ijca2016911283

@article{ 10.5120/ijca2016911283,
author = { Susovan Jana, Ranjan Parekh },
title = { Intra-class Recognition of Fruits using Color and Texture Features with Neural Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 11 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number11/25798-2016911283/ },
doi = { 10.5120/ijca2016911283 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:53:03.253870+05:30
%A Susovan Jana
%A Ranjan Parekh
%T Intra-class Recognition of Fruits using Color and Texture Features with Neural Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 11
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intra-class recognition of fruits using image processing and pattern recognition techniques, is a challenging task mainly because sub-types of the same fruit show a large amount of similarities between each other and hence more difficult to distinguish than when different types of fruits are involved (inter-class). The problem becomes more acute when the camera viewpoint also changes which tend to change the known characteristics of the fruits like contour shape. To solve this problem, this paper proposes a view point invariant solution for intra-class recognition of fruits by combining color and texture features and using a Neural Network (NN) classifier. Experimentations done on a dataset of 270 fruit images show satisfactory performance across different fruit types and sub-types.

References
  1. S. Mohamed Mansoor Roomi, R. Jyothi Priya, S.Bhumesh and P.Monisha, “Classification of Mangoes By Object Features and Contour Modeling”, International Conference on Machine Vision and Image Processing (MVIP), IEEE, 14-15 Dec. 2012, pp. 165-168.
  2. TheTimes of India, http://timesofindia.indiatimes.com/india/India-2nd-largest-fruit-producer-in-world/articleshow/50618234.cms
  3. Shiv Ram Dubey and Anand Singh Jalal, “Application of Image Processing in Fruit and Vegetable Analysis: A Review”, Journal of Intelligent Systems, ISSN (Online) 2191-026X, vol. 24, issue 4, 2014, pp. 405-424.
  4. Hossam M. Zawbaa, Maryam Hazman, Mona Abbass and Aboul Ella Hassanien, “Automatic fruit classification using random forest algorithm”, 14th International Conference on Hybrid Intelligent Systems (HIS), IEEE, 14-16 Dec. 2014, pp. 164 – 168.
  5. Abdulhamid Haidar, Haiwei Dong and Nikolaos Mavridis, “Image-Based Date Fruit Classification”, 4th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE, 3-5 Oct. 2012, pp. 357 -363.
  6. Shiv Ram Dubey and A S Jalal, “Robust Approach for Fruit and Vegetable Classification”, International Conference On Modeling Optimization and Computing, Elsevier, vol. 38, 2012, pp. 3449-3453.
  7. Md.Towhid Chowdhury, Md.Shariful Alam, Muhammad Asiful Hasan and Md.Imran Khan, “Vegetables detection from the glossary shop for the blind”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), e-ISSN: 2278-1676, p-ISSN: 2320-3331, vol. 8, issue 3 (Nov. - Dec. 2013), PP 43-53.
  8. Lv Jidong, Zhao De-An, Ji Wei and Ding Shihong, “Recognition of apple fruit in natural environment”, Optik - International Journal for Light and Electron Optics, Elsevier, vol. 127, issue 3, February 2016, pp. 1354–1362.
  9. N. Otsu, “A threshold selection method from gray-level histograms”, IEEE Trans on Sys. Man. Cyber. vol.9, issue 1, 1979, pp. 62-66.
  10. Rong Xiang, Yibin Ying and Huanyu Jiang, “Tests of a Recognition Algorithm for Clustered Tomatoes Based on Mathematical Morphology”, 6th International Congress on Image and Signal Processing (CISP), IEEE, vol. 1, 16-18 Dec. 2013, pp. 464 – 468.
  11. Computers and Optics in Food Inspection, http://www.cofilab.com/portfolio/
  12. R. M. Haralick, “Statistical and structural approaches to texture”, Proc. IEEE, vol. 67, issue 5, 1979, pp. 786-804.
Index Terms

Computer Science
Information Sciences

Keywords

Color Histogram Texture features Gray Level Co-occurrence Matrix Neural Network