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Pattern based Dimensionality Reduction Model for Age Classification

by V. Vijaya Kumar, Jangala Sasi Kiran, Gorti Satyanarayana Murty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 79 - Number 13
Year of Publication: 2013
Authors: V. Vijaya Kumar, Jangala Sasi Kiran, Gorti Satyanarayana Murty
10.5120/13800-1787

V. Vijaya Kumar, Jangala Sasi Kiran, Gorti Satyanarayana Murty . Pattern based Dimensionality Reduction Model for Age Classification. International Journal of Computer Applications. 79, 13 ( October 2013), 14-20. DOI=10.5120/13800-1787

@article{ 10.5120/13800-1787,
author = { V. Vijaya Kumar, Jangala Sasi Kiran, Gorti Satyanarayana Murty },
title = { Pattern based Dimensionality Reduction Model for Age Classification },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 79 },
number = { 13 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume79/number13/13800-1787/ },
doi = { 10.5120/13800-1787 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:52:53.434590+05:30
%A V. Vijaya Kumar
%A Jangala Sasi Kiran
%A Gorti Satyanarayana Murty
%T Pattern based Dimensionality Reduction Model for Age Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 79
%N 13
%P 14-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The two most popular statistical methods used to measure the textural information of images are the Grey Level Co-occurrence Matrix (GLCM) and Texture Units (TU) approaches. The novelty of the present paper is, it combines TU and GLCM features by deriving a new model called "Pattern based Second order Compressed Binary (PSCB) image" to classify human age in to four groups. The proposed PSCB model reduces the given 5 x 5 grey level image into a 2 x 2 binary image, while preserving the significant features of the texture. The proposed method intelligently compressed a 5x5 window into a 2x2 window and derived TU on them. Thus the derived TU also represents a TU of a 5x5 window. The TU of the proposed PSCB model ranges from 0 to 15, thus it overcomes the previous disadvantages in evaluating TU's.

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Index Terms

Computer Science
Information Sciences

Keywords

GLCM features Texture Unit Pattern compressed model

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