Intelligent classification models for food products basis on morphological, colour and texture features

Narendra, V G and Kamath, Priya (2017) Intelligent classification models for food products basis on morphological, colour and texture features. Acta Agronomica, 66 (4). pp. 1-21. ISSN 0120-2812

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Abstract

The aim of this research 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 are used to train the models for classification and detection. The best prediction accuracy is 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 is 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: Article
Uncontrolled Keywords: Algorithm, digital images, food classifiers, prediction accuracy, training/test.
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 22 Aug 2017 04:55
Last Modified: 22 Aug 2017 04:55
URI: http://eprints.manipal.edu/id/eprint/149564

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