Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12911
Title: A Method of Thai Main Dish and Soup Classification by Gray Level Co-Occurrence Matrix Algorithm
Authors: Neampradit P.
Charoenpong T.
Sueaseenak D.
Sukjamsri C.
Keywords: Support vector machines
Textures
Entropy-based
Entropy-based algorithm
Gray level co occurrence matrix(GLCM)
Gray level co-occurrence matrix
Sensitivity and specificity
Support vector machine techniques
Texture analysis
Three parameters
Entropy
Issue Date: 2018
Abstract: This paper presents a new method to classify Thai main dish and soup by Gray Level Co-occurrence Matrix (GLCM). An entropy-based algorithm is used to extract the food area from the background. A GLCM texture analysis algorithm is used to calculate features vector of food. The GLCM algorithm can calculate an energy, a homogeneity, and a correlation to be featured. The three parameters are used for classification. Support Vector Machine technique is used for classification. The experimental result showed 100 percent of sensitivity and specificity for main courses, 89.9 percent of sensitivity and specificity and 99.44 percent of accuracy. This is the first method that can classify Thai main dish and soup. © 2018 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/12911
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066604430&doi=10.1109%2fIEECON.2018.8712294&partnerID=40&md5=40faf7bd04de28c36c0c65853e1afe43
Appears in Collections:Scopus 1983-2021

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