Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval

Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper present...

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Main Authors: Abd.Rasid, Mamat, Norkhairani, Abdul Rawi, Mohd Fadzil, Abdul Kadir
Format: Article
Language:English
Published: Science and Engineering Research Support Society 2016
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Online Access:http://eprints.unisza.edu.my/7189/1/FH02-FIK-16-05471.jpg
http://eprints.unisza.edu.my/7189/
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spelling my-unisza-ir.71892022-09-13T04:33:07Z http://eprints.unisza.edu.my/7189/ Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval Abd.Rasid, Mamat Norkhairani, Abdul Rawi Mohd Fadzil, Abdul Kadir QA Mathematics Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%. Science and Engineering Research Support Society 2016 Article PeerReviewed image en http://eprints.unisza.edu.my/7189/1/FH02-FIK-16-05471.jpg Abd.Rasid, Mamat and Norkhairani, Abdul Rawi and Mohd Fadzil, Abdul Kadir (2016) Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval. International Journal of Multimedia and Ubiquitous Engineering, 11 (2). pp. 79-88. ISSN 1975-0080 [P]
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Abd.Rasid, Mamat
Norkhairani, Abdul Rawi
Mohd Fadzil, Abdul Kadir
Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
description Many textures based image retrieval researchers use global texture features for representing and retrieval of images from an image database. However, this leads to misrepresentation of local information leading to the inefficient image retrieval performance. This paper presents an approach to overcome the problem. The approach focuses on extracting local Haralick’s texture feature based on a predetermined region using the color co-occurrence matrix method, the selection of the ‘significant’ Haralik’s texture features and evaluation of the performance of the combination of the ‘significant’ features. The proposed method which is an Average Analysis and a well known method, Principal Component Analysis were applied to obtain ‘significant’ features. In order to compare the performance, a series of experiments were carried out for both methods, which is the proposed Average Analysis and the Principal Component Analysis. Experiments were performed on a 1000 selected images from the Coral image database which were divided into ten categories. Based on the experimental results, it is interesting to note that for the combination ‘significant’ features obtained from the proposed Average Analysis showed better retrieval performance compared to the Principal Component Analysis for almost all categories. This finding has an important implication in deciding the correct combination of ‘significant’ features for certain image properties. It has shown that the proposed method is able to produce less computational processing time due to a reduced amount of processing involved. The result is also compared to the previous researches and has shown an increase of an average precision from 8.5% to 26%.
format Article
author Abd.Rasid, Mamat
Norkhairani, Abdul Rawi
Mohd Fadzil, Abdul Kadir
author_facet Abd.Rasid, Mamat
Norkhairani, Abdul Rawi
Mohd Fadzil, Abdul Kadir
author_sort Abd.Rasid, Mamat
title Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
title_short Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
title_full Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
title_fullStr Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
title_full_unstemmed Average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
title_sort average analysis method in selecting haralick’s texture features on color co-occurrence matrix for texture based image retrieval
publisher Science and Engineering Research Support Society
publishDate 2016
url http://eprints.unisza.edu.my/7189/1/FH02-FIK-16-05471.jpg
http://eprints.unisza.edu.my/7189/
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