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Reseach Article

multiple representation of perceptual features for texture classification

Published on April 2012 by B. Aarthy, G. Tamilpavai, S. Tamilselvi
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 1
April 2012
Authors: B. Aarthy, G. Tamilpavai, S. Tamilselvi
6148c973-c388-49a9-8390-59658a7d4bce

B. Aarthy, G. Tamilpavai, S. Tamilselvi . multiple representation of perceptual features for texture classification. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 1 (April 2012), 1-5.

@article{
author = { B. Aarthy, G. Tamilpavai, S. Tamilselvi },
title = { multiple representation of perceptual features for texture classification },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 1 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/icon3c/number1/6000-1001/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A B. Aarthy
%A G. Tamilpavai
%A S. Tamilselvi
%T multiple representation of perceptual features for texture classification
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 1
%P 1-5
%D 2012
%I International Journal of Computer Applications
Abstract

Texture Classification plays a vital role in medical image, remote sensing image, pattern analysis for the past three decades. Eventhough it is three decades problem, still having a lot of scope in pattern analysis. Textural features corresponding to visual properties of texture are highly desirable for two reasons; they will be optimum in terms of feature selection and will be applicable to all kinds of textures. Some of the perceptual features are coarseness, contrast, direction and busyness. The aim of this paper is to present a new method to estimate these perceptual features. The proposal based on two representations: Original Image Representation and Autocorrelation Function Representation. These estimated perceptual features measures are applied to classification on large image data set, the well-known Brodatz database using k-nearest neighborhood classifier.

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Index Terms

Computer Science
Information Sciences

Keywords

Texture Classification Plays A Vital Role In Medical Image Remote Sensing Image Pattern Analysis For The Past Three Decades. Eventhough It Is Three Decades Problem Still Having A Lot Of Scope In Pattern Analysis. Textural Features Corresponding To Visual Properties Of Texture Are Highly Desirable For Two Reasons They Will Be Optimum In Terms Of Feature Selection And Will Be Applicable To All Kinds Of Textures. Some Of The Perceptual Features Are Coarseness Contrast Direction And Busyness. The Aim Of This Paper Is To Present A New Method To Estimate These Perceptual Features. The Proposal Based On Two Representations: Original Image Representation And Autocorrelation Function Representation. These Estimated Perceptual Features Measures Are Applied To Classification On Large Image Data Set The Well-known Brodatz Database Using K-nearest Neighborhood Classifier.