Comparative Study of Certain Classifiers for Variety Classification of Certain Thin and Thick Fabric Images

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Basavaraj S. Anami 1,* Mahantesh C. Elemmi 1

1. K.L.E. Institute of Technology, Hubballi,580030, India

* Corresponding author.


Received: 7 Sep. 2018 / Revised: 27 Sep. 2018 / Accepted: 17 Oct. 2018 / Published: 8 Jan. 2019

Index Terms

Texture features, Fabric images, Feature extraction, Thin fabric, Thick fabric


The proposed work gives a comparative study of three different classifiers, namely, decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) for variety classification of certain thin and thick fabric images. The textural features are used in the work. The overall classification rates of 85%, 86% and 94% are obtained for DT, SVM and ANN classifiers respectively. Better results for varieties of thick fabric images are obtained compared to the varieties of thin fabric images. Further, the ANN classifier has given good classification rate than DT and SVM classifiers. But, it is also observed that, DT classifier gives better results in case of varieties of thick fabric images. The work finds applications in apparel industry, cost estimation, setting the washing time, fashion design etc.

Cite This Paper

Basavaraj S. Anami, Mahantesh C. Elemmi, " Comparative Study of Certain Classifiers for Variety Classification of Certain Thin and Thick Fabric Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.1, pp. 54-61, 2019. DOI: 10.5815/ijigsp.2019.01.06


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