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Reseach Article

Image Retrieval using Multi Cascaded Features

by Hashem B. Jehlol, Mayyadah Jabbar Gailan, Anwer Subhi Abdulhussein Oleiwi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 9
Year of Publication: 2016
Authors: Hashem B. Jehlol, Mayyadah Jabbar Gailan, Anwer Subhi Abdulhussein Oleiwi
10.5120/ijca2016908132

Hashem B. Jehlol, Mayyadah Jabbar Gailan, Anwer Subhi Abdulhussein Oleiwi . Image Retrieval using Multi Cascaded Features. International Journal of Computer Applications. 134, 9 ( January 2016), 34-38. DOI=10.5120/ijca2016908132

@article{ 10.5120/ijca2016908132,
author = { Hashem B. Jehlol, Mayyadah Jabbar Gailan, Anwer Subhi Abdulhussein Oleiwi },
title = { Image Retrieval using Multi Cascaded Features },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 9 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number9/23946-2016908132/ },
doi = { 10.5120/ijca2016908132 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:34:29.729709+05:30
%A Hashem B. Jehlol
%A Mayyadah Jabbar Gailan
%A Anwer Subhi Abdulhussein Oleiwi
%T Image Retrieval using Multi Cascaded Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 9
%P 34-38
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper depends on Content Based Image Retrieval CBIR to retrieve desired images from a large images database. It is based on extracting cascade features from image such as color, shape and texture features. The Euclidean Distance is used to measure similarity of each feature and retrieve similar images from the image database. The proposed method contains three stages: in the first stage, the HSV color histogram from query image is found and the nearest 100 images are retrieved. In the second stage the shape features are extracted from edge detection and the nearest 50 image are retrieved. In the last stage the texture feature are found based on first order features and the nearest 10 images are retrieved. In this paper a mechanism for image retrieval based on cascading approach is developed to improve image retrieval performance and reduce the computational time required to retrieve images. It is found that cascading features with Euclidean distance give about 76% precession of image retrieval.

References
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Index Terms

Computer Science
Information Sciences

Keywords

CBIR Euclidean Distance HSV color histogram shape features texture feature.