TELKOMNIKA Telecommunication, Computing, Electronics and Control
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system
Subject
Content-based image retrieval system, Feature dimensionality reduction, Low-level visual feature, Principal component analysis
Description
In content-based image retrieval (CBIR) system, one approach of image
representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on principal component analysis (PCA) is implemented. Each image in a database is indexed using 174-dimensional feature vector comprising of 54-dimensional colour moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional gabor wavelet (GW48) and 40-dimensional wavelet moments (MW40).
The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.
representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on principal component analysis (PCA) is implemented. Each image in a database is indexed using 174-dimensional feature vector comprising of 54-dimensional colour moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional gabor wavelet (GW48) and 40-dimensional wavelet moments (MW40).
The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.
Creator
Oluwole A. Adegbola, Ismail A. Adeyemo, Folasade A. Semire, Segun I. Popoola, Aderemi A. Atayero
Source
DOI: 10.12928/TELKOMNIKA.v18i4.11176
Publisher
Universitas Ahmad Dahlan
Date
August 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Oluwole A. Adegbola, Ismail A. Adeyemo, Folasade A. Semire, Segun I. Popoola, Aderemi A. Atayero, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system,” Repository Horizon University Indonesia, accessed February 5, 2025, https://repository.horizon.ac.id/items/show/3918.
A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system,” Repository Horizon University Indonesia, accessed February 5, 2025, https://repository.horizon.ac.id/items/show/3918.