Performance Analysis of Texture Image Classification Using Wavelet Feature

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Dolly Choudhary 1,* Ajay Kumar Singh 1 Shamik Tiwari 1 V. P. Shukla 2

1. Deptt. Of Computer Sc. & Engineering Faculty of Engineering & Tech., MITS Laxmangarh,(India)

2. Deptt. Of Mathematics Faculty of Engineering & Tech. MITS Laxmangarh,(India)

* Corresponding author.


Received: 13 Sep. 2012 / Revised: 26 Oct. 2012 / Accepted: 3 Dec. 2012 / Published: 8 Jan. 2013

Index Terms

Multiclass, Wavelet, Feature Extraction, Neural Network, Naïve Bays, K-NN


This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.

Cite This Paper

Dolly Choudhary,Ajay Kumar Singh,Shamik Tiwari,V P Shukla,"Performance Analysis of Texture Image Classification Using Wavelet Feature", IJIGSP, vol.5, no.1, pp.58-63, 2013. DOI: 10.5815/ijigsp.2013.01.08


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