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GaborNet: investigating the importance of color space, scale and orientation for image classification

Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the...

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Detalles Bibliográficos
Autores principales: Rimiru, Richard M., Gateri, Judy, Kimwele, Micheal W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044268/
https://www.ncbi.nlm.nih.gov/pubmed/35494856
http://dx.doi.org/10.7717/peerj-cs.890
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author Rimiru, Richard M.
Gateri, Judy
Kimwele, Micheal W.
author_facet Rimiru, Richard M.
Gateri, Judy
Kimwele, Micheal W.
author_sort Rimiru, Richard M.
collection PubMed
description Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the images. Examination of the characteristics is done for ensuring the searching and retrieval of proportional images from the image database. Majority of the datasets utilized for retrieval lean towards to comprise colored images. The colored images are regarded as in RGB (Red, Green, Blue) form. Most colored images use the RGB image for classifying the images. The research presents the transformation of RGB to other color spaces, extraction of features using different color spaces techniques, Gabor filter and use Convolutional Neural Networks for retrieval to find the most efficient combination. The model is also known as Gabor Convolution Network. Even though the notion of the Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor-based CNN which produces high recognition efficiency. In this paper, Gabor Convolutional Networks (GCNs or GaborNet), with different color spaces are used to examine which combination is efficient to retrieve natural images. An extensive experiment using Cifar 10 dataset was made and comparison of simple CNN, ResNet 50 and GCN model was also made. The models were evaluated through a several statistical analysis based on accuracy, precision, recall, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results shows GaborNet model effectively retrieve images with 99.68% of AUC and 99.09% of Recall. The results also shows different images are effectively retrieved using different color space. Therefore research concluded it is very significance to transform images to different color space and use GaborNet for effective retrieval.
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spelling pubmed-90442682022-04-28 GaborNet: investigating the importance of color space, scale and orientation for image classification Rimiru, Richard M. Gateri, Judy Kimwele, Micheal W. PeerJ Comput Sci Algorithms and Analysis of Algorithms Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the images. Examination of the characteristics is done for ensuring the searching and retrieval of proportional images from the image database. Majority of the datasets utilized for retrieval lean towards to comprise colored images. The colored images are regarded as in RGB (Red, Green, Blue) form. Most colored images use the RGB image for classifying the images. The research presents the transformation of RGB to other color spaces, extraction of features using different color spaces techniques, Gabor filter and use Convolutional Neural Networks for retrieval to find the most efficient combination. The model is also known as Gabor Convolution Network. Even though the notion of the Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor-based CNN which produces high recognition efficiency. In this paper, Gabor Convolutional Networks (GCNs or GaborNet), with different color spaces are used to examine which combination is efficient to retrieve natural images. An extensive experiment using Cifar 10 dataset was made and comparison of simple CNN, ResNet 50 and GCN model was also made. The models were evaluated through a several statistical analysis based on accuracy, precision, recall, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results shows GaborNet model effectively retrieve images with 99.68% of AUC and 99.09% of Recall. The results also shows different images are effectively retrieved using different color space. Therefore research concluded it is very significance to transform images to different color space and use GaborNet for effective retrieval. PeerJ Inc. 2022-02-25 /pmc/articles/PMC9044268/ /pubmed/35494856 http://dx.doi.org/10.7717/peerj-cs.890 Text en © 2022 Rimiru et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Rimiru, Richard M.
Gateri, Judy
Kimwele, Micheal W.
GaborNet: investigating the importance of color space, scale and orientation for image classification
title GaborNet: investigating the importance of color space, scale and orientation for image classification
title_full GaborNet: investigating the importance of color space, scale and orientation for image classification
title_fullStr GaborNet: investigating the importance of color space, scale and orientation for image classification
title_full_unstemmed GaborNet: investigating the importance of color space, scale and orientation for image classification
title_short GaborNet: investigating the importance of color space, scale and orientation for image classification
title_sort gabornet: investigating the importance of color space, scale and orientation for image classification
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044268/
https://www.ncbi.nlm.nih.gov/pubmed/35494856
http://dx.doi.org/10.7717/peerj-cs.890
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