Enhanced Performance of Multi Class Classification of Anonymous Noisy Images

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Ajay Kumar Singh 1,* V. P. Shukla 2 S. R. Biradar 1 Shamik Tiwari 1

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.

DOI: https://doi.org/10.5815/ijigsp.2014.03.04

Received: 2 Nov. 2013 / Revised: 30 Nov. 2013 / Accepted: 3 Jan. 2014 / Published: 8 Feb. 2014

Index Terms

Statistical texture, feature extraction, noise detection, multiclass classification, neural network


An important constituents for image classification is the identification of significant characterstics about the specific class to distinguish intra class variations. Since performance of the classifiers is affected in the presence of noise, so selection of discriminative features is an important phase in classification. This superfluous information i.e. noise, e.g. additive noise may occur in images due to image sensors i.e. of the constant noise level in dark areas of the image or salt & pepper noise may be caused by analog to digitals conversion and bit error transmission etc.. Detection of noise is also very essential in the images for choosing appropriate filter. This paper presents an experimental assessment of neural classifier in terms of classification accuracy under three different constraints of images without noise, in presence of unknown noise and after elimination of noise.

Cite This Paper

Ajay Kumar Singh, V P Shukla, Sangappa R. Biradar, Shamik Tiwari,"Enhanced Performance of Multi Class Classification of Anonymous Noisy Images", IJIGSP, vol.6, no.3, pp.27-34, 2014. DOI: 10.5815/ijigsp.2014.03.04


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