DocumentCode :
682490
Title :
A classification method of coconut wood quality based on Gray Level Co-occurrence matrices
Author :
Pramunendar, Ricardus Anggi ; Supriyanto, Catur ; Novianto, Dwi Hermawan ; Yuwono, Ignatius Ngesti ; Shidik, Guruh Fajar ; Andono, Pulung Nurtantio
Author_Institution :
Fac. of Comput. Sci., Univ. of Dian Nuswantoro, Semarang, Indonesia
fYear :
2013
fDate :
25-27 Nov. 2013
Firstpage :
254
Lastpage :
257
Abstract :
Coconut tree grows rapidly in tropical region such as Indonesia. Coconut wood is used as alternative or complementary raw material for housing or making furniture. Abundant coconut trees are planted, however the utilization of coconut wood as raw material for furniture is still very rare in Indonesia. This is caused by the low quality of coconut wood, since it has not found adequate technology for the processing of coconut wood. This paper presents our experimental work on coconut wood quality classification using self-tuning MLP classifier (AutoMLP) and Support Vector Machine (SVM). For SVM classifier we used the LibSVM library, available in RapidMiner. The Gray-Level Co-occurrence Matrix (GLCM) is used to extract the texture features of coconut wood images. Experiment result shows that AutoMLP gives the best accuracy rate at 78.82%, which is slightly better than 77.06% of SVM.
Keywords :
feature extraction; furniture industry; image classification; image texture; matrix algebra; multilayer perceptrons; quality control; support vector machines; wood; AutoMLP; GLCM; LibSVM library; RapidMiner; SVM classifier; coconut wood images; coconut wood quality classification method; furniture industry; gray level co-occurrence matrices; self-tuning MLP classifier; support vector machine; texture feature extraction; Support vector machines; artificial neural network; coconut wood classification; gray level co-occurrence matrix; support vector machine; texture features;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics, Biomimetics, and Intelligent Computational Systems (ROBIONETICS), 2013 IEEE International Conference on
Conference_Location :
Jogjakarta
Print_ISBN :
978-1-4799-1206-3
Type :
conf
DOI :
10.1109/ROBIONETICS.2013.6743614
Filename :
6743614
Link To Document :
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