Texture image retrieval using a classification and contourlet-based features
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911792539631616 |
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| author | Rouhafzay, Asal Baaziz, Nadia Allili, Mohand Said |
| author_facet | Rouhafzay, Asal Baaziz, Nadia Allili, Mohand Said |
| contents | In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images. This is achieved by using a new image representation based on the RCT-Plus transform which is a novel variant of the Redundant Contourlet transform that extracts a richer directional information in the image. Moreover, the process of image search is improved through a learning-based approach where the images of the database are classified using an adapted similarity metric to the statistical modeling of the RCT-Plus transform. A query is then first classified to select the best texture class after which the retained class images are ranked to select top ones. By this, we have achieved significant improvements in the retrieval rates compared to previous CBIR schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06048 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Texture image retrieval using a classification and contourlet-based features Rouhafzay, Asal Baaziz, Nadia Allili, Mohand Said Computer Vision and Pattern Recognition Machine Learning In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images. This is achieved by using a new image representation based on the RCT-Plus transform which is a novel variant of the Redundant Contourlet transform that extracts a richer directional information in the image. Moreover, the process of image search is improved through a learning-based approach where the images of the database are classified using an adapted similarity metric to the statistical modeling of the RCT-Plus transform. A query is then first classified to select the best texture class after which the retained class images are ranked to select top ones. By this, we have achieved significant improvements in the retrieval rates compared to previous CBIR schemes. |
| title | Texture image retrieval using a classification and contourlet-based features |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.06048 |