Author(s): Suresha M
Journal: International Journal of Image Processing and Visual Communication
ISSN 2319-1724
Volume: 1;
Issue: 2;
Start page: 42;
Date: 2012;
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Keywords: Decision Tree Classifier | GLCM | Mean-around Features | Texture Features | Vegetables Classification.
ABSTRACT
In this paper, we have proposed a method for classification of vegetables based on the extraction of texture properties. The work has been carried out using watershed for segmentation. The vegetable texture 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. Classification is done using Mean around features, Gray level Co-occurrence matrix (GLCM) features and combined (Mean around-GLCM) features. Decision trees classifier is used for classification of vegetables in to eight classes. Splitting rules for growing a decision tree included in this work are Gini diversity index(gdi), Twoing rule, and Entropy. Results obtained from the proposed method are well accepted and solutions are good agreement with the experts. Proposed approach is experimented on vegetable data set using cross validation and found good success rate.
Journal: International Journal of Image Processing and Visual Communication
ISSN 2319-1724
Volume: 1;
Issue: 2;
Start page: 42;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Decision Tree Classifier | GLCM | Mean-around Features | Texture Features | Vegetables Classification.
ABSTRACT
In this paper, we have proposed a method for classification of vegetables based on the extraction of texture properties. The work has been carried out using watershed for segmentation. The vegetable texture 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. Classification is done using Mean around features, Gray level Co-occurrence matrix (GLCM) features and combined (Mean around-GLCM) features. Decision trees classifier is used for classification of vegetables in to eight classes. Splitting rules for growing a decision tree included in this work are Gini diversity index(gdi), Twoing rule, and Entropy. Results obtained from the proposed method are well accepted and solutions are good agreement with the experts. Proposed approach is experimented on vegetable data set using cross validation and found good success rate.
