Biodiversity image retrieval framework for monogeneans
In this paper, we propose a framework for Biodiversity Image Retrieval using Content Based Image Retrieval (CBIR) and structured vocabulary. The monogenean images used in this paper belong to the order Dactylogyridea Bychowsky, 1937. We use illustrations of monogenean haptoral bar diagnostic hard pa...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Taylor & Francis Group
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/46772 |
| _version_ | 1848757653614886912 |
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| author | Abu, Arpah Lim, Lee Hong Sidhu, Amandeep Dhillon, Sarinder |
| author_facet | Abu, Arpah Lim, Lee Hong Sidhu, Amandeep Dhillon, Sarinder |
| author_sort | Abu, Arpah |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a framework for Biodiversity Image Retrieval using Content Based Image Retrieval (CBIR) and structured vocabulary. The monogenean images used in this paper belong to the order Dactylogyridea Bychowsky, 1937. We use illustrations of monogenean haptoral bar diagnostic hard part to demonstrate how textual and content-based information can be integrated in building a better retrieval system. In this approach, an ontology-based image annotation and retrieval is developed to support the CBIR. Using ontology in combination with CBIR reduces the number of images needed in the training set, utilizing the provided user parameters. Efficiency of retrieval of the proposed approach is calculated using the relevance ranking and classification error rate of the retrieved images; and the efficiency of overall retrieval performance. |
| first_indexed | 2025-11-14T09:31:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-46772 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:31:31Z |
| publishDate | 2013 |
| publisher | Taylor & Francis Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-467722017-09-13T16:05:30Z Biodiversity image retrieval framework for monogeneans Abu, Arpah Lim, Lee Hong Sidhu, Amandeep Dhillon, Sarinder monogenean image illustrations biodiversity image retrieval content-based image retrieval ontology In this paper, we propose a framework for Biodiversity Image Retrieval using Content Based Image Retrieval (CBIR) and structured vocabulary. The monogenean images used in this paper belong to the order Dactylogyridea Bychowsky, 1937. We use illustrations of monogenean haptoral bar diagnostic hard part to demonstrate how textual and content-based information can be integrated in building a better retrieval system. In this approach, an ontology-based image annotation and retrieval is developed to support the CBIR. Using ontology in combination with CBIR reduces the number of images needed in the training set, utilizing the provided user parameters. Efficiency of retrieval of the proposed approach is calculated using the relevance ranking and classification error rate of the retrieved images; and the efficiency of overall retrieval performance. 2013 Journal Article http://hdl.handle.net/20.500.11937/46772 10.1080/14772000.2012.761655 Taylor & Francis Group restricted |
| spellingShingle | monogenean image illustrations biodiversity image retrieval content-based image retrieval ontology Abu, Arpah Lim, Lee Hong Sidhu, Amandeep Dhillon, Sarinder Biodiversity image retrieval framework for monogeneans |
| title | Biodiversity image retrieval framework for monogeneans |
| title_full | Biodiversity image retrieval framework for monogeneans |
| title_fullStr | Biodiversity image retrieval framework for monogeneans |
| title_full_unstemmed | Biodiversity image retrieval framework for monogeneans |
| title_short | Biodiversity image retrieval framework for monogeneans |
| title_sort | biodiversity image retrieval framework for monogeneans |
| topic | monogenean image illustrations biodiversity image retrieval content-based image retrieval ontology |
| url | http://hdl.handle.net/20.500.11937/46772 |