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 |
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| 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 |
| DOI: | 10.1093/mnras/staf230 |
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