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Content based Image Retrieval using Model Approach

by Kunal Shriwas, Vaqar Ansari
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 8
Year of Publication: 2016
Authors: Kunal Shriwas, Vaqar Ansari
10.5120/ijais2016451542

Kunal Shriwas, Vaqar Ansari . Content based Image Retrieval using Model Approach. International Journal of Applied Information Systems. 10, 8 ( April 2016), 27-32. DOI=10.5120/ijais2016451542

@article{ 10.5120/ijais2016451542,
author = { Kunal Shriwas, Vaqar Ansari },
title = { Content based Image Retrieval using Model Approach },
journal = { International Journal of Applied Information Systems },
issue_date = { April 2016 },
volume = { 10 },
number = { 8 },
month = { April },
year = { 2016 },
issn = { 2249-0868 },
pages = { 27-32 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number8/884-2016451542/ },
doi = { 10.5120/ijais2016451542 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:02:58.487984+05:30
%A Kunal Shriwas
%A Vaqar Ansari
%T Content based Image Retrieval using Model Approach
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 8
%P 27-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to rapid development of digital and information technologies, more multimedia information is generated and available in digital form from varieties of resources around the world. Content based image retrieval systems (CBIR) are designed to allow users to search images in large databases which match closely with user’s query image. Proposed framework consists of all three features to achieve better retrieval results. The color feature is extracted by quantifying the HSV color space and the color attribute like mean value, standard deviation and the image bitmap of HSV color space. The edge feature are obtained by edge histogram descriptor. Texture features are obtained by entropy based gray level co-occurrence matrix (GLCM). Euclidian distance is used to find similarity measurement between query image and database images.

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Index Terms

Computer Science
Information Sciences

Keywords

Content based image retrieval (CBIR); HSV; Image binary bitmap; Gray level co-occurrence matrix (GLCM); Edge histogram descriptor (EHD).

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