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Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types

Punit Soni Orcid Logo, Mandeep Singh Orcid Logo, Purushottam Sharma Orcid Logo, Tajinder Kumar Orcid Logo, Cheng Cheng Orcid Logo, Rajender Kumar Orcid Logo, Mrinal Paliwal Orcid Logo

Journal of Electrical and Computer Engineering, Volume: 2025, Issue: 1

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.1155/jece/3889925

Abstract

There is a growing trend for using content-based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will...

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Published in: Journal of Electrical and Computer Engineering
ISSN: 2090-0147 2090-0155
Published: Wiley 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69424
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Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature-based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel-1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%&#x2013;3.6% across capabilities. 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spelling 2025-06-02T15:10:49.9547993 v2 69424 2025-05-06 Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2025-05-06 MACS There is a growing trend for using content-based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature-based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel-1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework. Journal Article Journal of Electrical and Computer Engineering 2025 1 Wiley 2090-0147 2090-0155 CBIR; Euclidean; features; fusion; Hausdrauff; HOG; multifeatures; SIFT; SURF 19 5 2025 2025-05-19 10.1155/jece/3889925 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) Engineering and Physical Sciences Research Council. Grant Number: EP/W020408/1; Swansea University 2025-06-02T15:10:49.9547993 2025-05-06T10:43:12.5184277 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Punit Soni 0000-0002-3805-486x 1 Mandeep Singh 0009-0008-7745-8016 2 Purushottam Sharma 0000-0002-8037-7152 3 Tajinder Kumar 0000-0002-7079-7065 4 Cheng Cheng 0000-0003-0371-9646 5 Rajender Kumar 0000-0001-7334-729x 6 Mrinal Paliwal 0000-0003-3063-1578 7 69424__34298__b9a321bb10f143e6991fac80f01ee557.pdf 69424.VoR.pdf 2025-05-19T12:04:09.2975324 Output 5232288 application/pdf Version of Record true Copyright © 2025 Punit Soni et al. This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
spellingShingle Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
Cheng Cheng
title_short Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
title_full Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
title_fullStr Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
title_full_unstemmed Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
title_sort Multifeature Fusion for Enhanced Content‐Based Image Retrieval Across Diverse Data Types
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Punit Soni
Mandeep Singh
Purushottam Sharma
Tajinder Kumar
Cheng Cheng
Rajender Kumar
Mrinal Paliwal
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container_title Journal of Electrical and Computer Engineering
container_volume 2025
container_issue 1
publishDate 2025
institution Swansea University
issn 2090-0147
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doi_str_mv 10.1155/jece/3889925
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description There is a growing trend for using content-based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature-based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel-1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework.
published_date 2025-05-19T07:38:38Z
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score 11.08895

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