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Efficient Modeling of Visual Art Color Image Clustering

by Y. Poornima, P. S. Hiremath
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 91 - Number 11
Year of Publication: 2014
Authors: Y. Poornima, P. S. Hiremath
10.5120/15926-5192

Y. Poornima, P. S. Hiremath . Efficient Modeling of Visual Art Color Image Clustering. International Journal of Computer Applications. 91, 11 ( April 2014), 27-32. DOI=10.5120/15926-5192

@article{ 10.5120/15926-5192,
author = { Y. Poornima, P. S. Hiremath },
title = { Efficient Modeling of Visual Art Color Image Clustering },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 91 },
number = { 11 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume91/number11/15926-5192/ },
doi = { 10.5120/15926-5192 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:12:29.215032+05:30
%A Y. Poornima
%A P. S. Hiremath
%T Efficient Modeling of Visual Art Color Image Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 91
%N 11
%P 27-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Although there has been massive research work being conducted in the area of content-based image retrieval (CBIR) system using various sophisticated techniques, very little work has been witnessed for visual art images. From the literatures, it has also been witnessed that clustering algorithm has played a big role in justifying the outcome of various CBIR system. The objective of the proposed system is to introduce a new clustering technique which is implemented over a large set of visual art images. The proposed algorithm is implemented and its performance is measured with respect to two performance parameter namely,. recall and precision. The accomplished outcome of the study is also compared with two conventional clustering techniques that are frequently seen on literatures to understand where the proposed system stands. The accomplished results were seen to outperform conventional clustering technique.

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Index Terms

Computer Science
Information Sciences

Keywords

Content based image retrieval system visual art image K-Means algorithm Clustering Techniques Block Truncation Coding.

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