Intelligent Classification models for Food products basis on Morphological,Colour and Texture features

Narendra, V G (2015) Intelligent Classification models for Food products basis on Morphological,Colour and Texture features. In: VBFoodNet2015, 24/11/2015, Nha Trnang University Nha Trnag Vietnam.

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Abstract

The aim of this paper is to build a supervised intelligent classification model of food products such as Biscuits, Cereals, Vegetables, Edible nuts and etc., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features were used to train the models for classification and detection. The best prediction accuracy was obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). The percentage of correctly classified instances was very high in these models and ranged from 80% to 96% for the training/test set and up to 95% for the validation set.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Classification models, Food products, Morphological features, Colour features, Texture features.
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 08 Feb 2016 15:54
Last Modified: 08 Feb 2016 15:54
URI: http://eprints.manipal.edu/id/eprint/145188

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