Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV

Image retrieval forms a major problem when a large database is considered. Content Base Image Retrieval (CBIR) makes use of the available visual features of the image and helps in retrieving similar image as that of the query image. In the CBIR method, each image stored in the database has its fe...

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Main Author:	H.M Alzoubi, Amera
Format:	Thesis
Language:	English English English
Published:	2015
Subjects:	TA1501-1820 Applied optics Photonics
Online Access:	http://eprints.uthm.edu.my/1295/2/AMERA%20H.M%20ALZOUBI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1295/1/24p%20AMERA%20H.M%20ALZOUBI.pdf http://eprints.uthm.edu.my/1295/3/AMERA%20H.M%20ALZOUBI%20WATERMARK.pdf
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spelling	my-uthm-ep.12952021-10-03T06:13:38Z Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV 2015-02 H.M Alzoubi, Amera TA1501-1820 Applied optics. Photonics Image retrieval forms a major problem when a large database is considered. Content Base Image Retrieval (CBIR) makes use of the available visual features of the image and helps in retrieving similar image as that of the query image. In the CBIR method, each image stored in the database has its features extracted and compared to the features of the query image. Thus, it involves two processes, feature extraction and feature matching. In this thesis, four techniques have been used, which are the Average of Red, Green and Blue Color Channels (Average RGB), Local Color Histogram (LCH), Global Color Histogram (GCH) and Color Moment of Hue, Saturation and Brightness Value (HSV) to retrieve relevant images based on colour. These techniques are applied on the collection of three images chosen randomly from each class of Wang images database. The performance of each technique has been individually evaluated, in terms of Execution Time, Precision, Recall, Accuracy, Redundancy Factor and Fall Rate. The results were then analysed and compared. The comparison was shown in bar graphs that the Average RGB technique has the best performance, where it obtained high accuracy. As a conclusion to the report, this comparative study contributes to the image searching field, by measuring the performance for several CBIR techniques using more commonly used parameters. 2015-02 Thesis http://eprints.uthm.edu.my/1295/ http://eprints.uthm.edu.my/1295/2/AMERA%20H.M%20ALZOUBI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1295/1/24p%20AMERA%20H.M%20ALZOUBI.pdf text en public http://eprints.uthm.edu.my/1295/3/AMERA%20H.M%20ALZOUBI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TA1501-1820 Applied optics Photonics
spellingShingle	TA1501-1820 Applied optics Photonics H.M Alzoubi, Amera Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
description	Image retrieval forms a major problem when a large database is considered. Content Base Image Retrieval (CBIR) makes use of the available visual features of the image and helps in retrieving similar image as that of the query image. In the CBIR method, each image stored in the database has its features extracted and compared to the features of the query image. Thus, it involves two processes, feature extraction and feature matching. In this thesis, four techniques have been used, which are the Average of Red, Green and Blue Color Channels (Average RGB), Local Color Histogram (LCH), Global Color Histogram (GCH) and Color Moment of Hue, Saturation and Brightness Value (HSV) to retrieve relevant images based on colour. These techniques are applied on the collection of three images chosen randomly from each class of Wang images database. The performance of each technique has been individually evaluated, in terms of Execution Time, Precision, Recall, Accuracy, Redundancy Factor and Fall Rate. The results were then analysed and compared. The comparison was shown in bar graphs that the Average RGB technique has the best performance, where it obtained high accuracy. As a conclusion to the report, this comparative study contributes to the image searching field, by measuring the performance for several CBIR techniques using more commonly used parameters.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	H.M Alzoubi, Amera
author_facet	H.M Alzoubi, Amera
author_sort	H.M Alzoubi, Amera
title	Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
title_short	Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
title_full	Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
title_fullStr	Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
title_full_unstemmed	Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
title_sort	comparative analysis of image search algorithm using average rgb, local color histogram, global color histogram and color moment hsv
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2015
url	http://eprints.uthm.edu.my/1295/2/AMERA%20H.M%20ALZOUBI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1295/1/24p%20AMERA%20H.M%20ALZOUBI.pdf http://eprints.uthm.edu.my/1295/3/AMERA%20H.M%20ALZOUBI%20WATERMARK.pdf
_version_	1747830765073203200

Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV

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