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Image Retrieval Based On Color and Texture Features of the Image Sub-blocks

by Ch.Kavitha, Dr.B.Prabhakara Rao, Dr.A.Govardhan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 15 - Number 7
Year of Publication: 2011
Authors: Ch.Kavitha, Dr.B.Prabhakara Rao, Dr.A.Govardhan
10.5120/1958-2619

Ch.Kavitha, Dr.B.Prabhakara Rao, Dr.A.Govardhan . Image Retrieval Based On Color and Texture Features of the Image Sub-blocks. International Journal of Computer Applications. 15, 7 ( February 2011), 33-37. DOI=10.5120/1958-2619

@article{ 10.5120/1958-2619,
author = { Ch.Kavitha, Dr.B.Prabhakara Rao, Dr.A.Govardhan },
title = { Image Retrieval Based On Color and Texture Features of the Image Sub-blocks },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 15 },
number = { 7 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume15/number7/1958-2619/ },
doi = { 10.5120/1958-2619 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:03:33.430042+05:30
%A Ch.Kavitha
%A Dr.B.Prabhakara Rao
%A Dr.A.Govardhan
%T Image Retrieval Based On Color and Texture Features of the Image Sub-blocks
%J International Journal of Computer Applications
%@ 0975-8887
%V 15
%N 7
%P 33-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays people are interested in using digital images. So the size of the image database is increasing enormously. Lot of interest is paid to find images in the database. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color and texture are two important visual features of an image. So, an efficient image retrieval technique which uses local color and texture features is proposed. An image is partitioned into sub-blocks of equal size as a first step. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by cumulative histogram. Texture of each sub-block is obtained by using gray level co-occurrence matrix. A one to one matching scheme is used to compare the query and target image. Euclidean distance is used in retrieving the similar images. The efficiency of the method is demonstrated with the results.

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Index Terms

Computer Science
Information Sciences

Keywords

Image retrieval color texture cumulative histogram gray level co-occurrence matrix

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