Academic Journal
Content-based image retrieval using COSFIRE descriptors with application to radio astronomy
| Τίτλος: | Content-based image retrieval using COSFIRE descriptors with application to radio astronomy |
|---|---|
| Συγγραφείς: | Ndung’u, Steven, Grobler, Trienko, Wijnholds, Stefan J., Azzopardi, George |
| Στοιχεία εκδότη: | Oxford University Press |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Content-based image retrieval, Image processing -- Digital techniques, Radio astronomy, Imaging systems in astronomy, Pattern recognition systems -- Data processing |
| Περιγραφή: | The morphologies of astronomical sources are highly complex, making it essential not only to classify the identified sources into their predefined categories but also to determine the sources that are most similar to a given query source. Image-based retrieval is essential, as it allows an astronomer with a source under study to ask a computer to sift through the large archived database of sources to find the most similar ones. This is of particular interest if the source under study does not fall into a ‘known’ category (anomalous). Our work uses the trainable COSFIRE (Combination of Shifted Filter Responses) approach for image retrieval. COSFIRE filters are automatically configured to extract the hyperlocal geometric arrangements that uniquely describe the morphological characteristics of patterns of interest in a given image; in this case astronomical sources. This is achieved by automatically examining the shape properties of a given prototype source in an image, which ultimately determines the selectivity of a COSFIRE filter. We further utilize hashing techniques, which are efficient in terms of required computation and storage, enabling scalability in handling large data sets in the image retrieval process. We evaluated the effectiveness of our approach by conducting experiments on a benchmark data set of radio galaxies, containing 1180 training images and 404 test images. Notably, our approach achieved a mean average precision of 91 per cent for image retrieval, surpassing both DenseNet-161 and group-equivariant convolutional neural networks (G-CNNs). Moreover, our approach is significantly more computationally efficient compared to both DenseNet-161 and G-CNNs. ; peer-reviewed |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/132584 |
| DOI: | 10.1093/mnras/staf230 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/132584 https://doi.org/10.1093/mnras/staf230 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Αριθμός Καταχώρησης: | edsbas.1D7574F1 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar/handle/123456789/132584# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Content-based image retrieval using COSFIRE descriptors with application to radio astronomy – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ndung%27u%2C+Steven%22">Ndung’u, Steven</searchLink><br /><searchLink fieldCode="AR" term="%22Grobler%2C+Trienko%22">Grobler, Trienko</searchLink><br /><searchLink fieldCode="AR" term="%22Wijnholds%2C+Stefan+J%2E%22">Wijnholds, Stefan J.</searchLink><br /><searchLink fieldCode="AR" term="%22Azzopardi%2C+George%22">Azzopardi, George</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: Oxford University Press – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Content-based+image+retrieval%22">Content-based image retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing+--+Digital+techniques%22">Image processing -- Digital techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Radio+astronomy%22">Radio astronomy</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems+in+astronomy%22">Imaging systems in astronomy</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems+--+Data+processing%22">Pattern recognition systems -- Data processing</searchLink> – Name: Abstract Label: Description Group: Ab Data: The morphologies of astronomical sources are highly complex, making it essential not only to classify the identified sources into their predefined categories but also to determine the sources that are most similar to a given query source. Image-based retrieval is essential, as it allows an astronomer with a source under study to ask a computer to sift through the large archived database of sources to find the most similar ones. This is of particular interest if the source under study does not fall into a ‘known’ category (anomalous). Our work uses the trainable COSFIRE (Combination of Shifted Filter Responses) approach for image retrieval. COSFIRE filters are automatically configured to extract the hyperlocal geometric arrangements that uniquely describe the morphological characteristics of patterns of interest in a given image; in this case astronomical sources. This is achieved by automatically examining the shape properties of a given prototype source in an image, which ultimately determines the selectivity of a COSFIRE filter. We further utilize hashing techniques, which are efficient in terms of required computation and storage, enabling scalability in handling large data sets in the image retrieval process. We evaluated the effectiveness of our approach by conducting experiments on a benchmark data set of radio galaxies, containing 1180 training images and 404 test images. Notably, our approach achieved a mean average precision of 91 per cent for image retrieval, surpassing both DenseNet-161 and group-equivariant convolutional neural networks (G-CNNs). Moreover, our approach is significantly more computationally efficient compared to both DenseNet-161 and G-CNNs. ; peer-reviewed – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://www.um.edu.mt/library/oar/handle/123456789/132584 – Name: DOI Label: DOI Group: ID Data: 10.1093/mnras/staf230 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar/handle/123456789/132584<br />https://doi.org/10.1093/mnras/staf230 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. – Name: AN Label: Accession Number Group: ID Data: edsbas.1D7574F1 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/mnras/staf230 Languages: – Text: English Subjects: – SubjectFull: Content-based image retrieval Type: general – SubjectFull: Image processing -- Digital techniques Type: general – SubjectFull: Radio astronomy Type: general – SubjectFull: Imaging systems in astronomy Type: general – SubjectFull: Pattern recognition systems -- Data processing Type: general Titles: – TitleFull: Content-based image retrieval using COSFIRE descriptors with application to radio astronomy Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ndung’u, Steven – PersonEntity: Name: NameFull: Grobler, Trienko – PersonEntity: Name: NameFull: Wijnholds, Stefan J. – PersonEntity: Name: NameFull: Azzopardi, George IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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