Publication:
A Method of Thai Main Dish and Soup Classification by Gray Level Co-Occurrence Matrix Algorithm

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Issued Date

2018

Resource Type

Conference Paper

File Type

application/pdf

DOI

10.1109/IEECON.2018.8712294

Other identifier(s)

2-s2.0-85066604430

Rights Holder(s)

Scopus

Bibliographic Citation

iEECON 2018 - 6th International Electrical Engineering Congress.

Suggested Citation

Neampradit P., Charoenpong T., Sueaseenak D., Sukjamsri C. A Method of Thai Main Dish and Soup Classification by Gray Level Co-Occurrence Matrix Algorithm. iEECON 2018 - 6th International Electrical Engineering Congress.. doi:10.1109/IEECON.2018.8712294 Retrieved from: https://hdl.handle.net/20.500.14740/3887

Title

A Method of Thai Main Dish and Soup Classification by Gray Level Co-Occurrence Matrix Algorithm

Author(s)

Neampradit P.
 Charoenpong T.
 Sueaseenak D.
 Sukjamsri C.

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.

Subject(s)

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

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URI

https://hdl.handle.net/20.500.14740/3887

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Scopus: Year 1983-2021

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