Fadzli, S. A. and Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544 2011. Ontology-based indexing of annotated images using semantic DNA and vector space model. Presented at: 2011 International Conference on Semantic Technology and Information Retrieval (STAIR'11), Putrajaya, Malaysia, 28-28 June 2011. Published in: Noah, S. A. M., Omar, N., Crestani, F., Mohd, M., Aziz, M. J. A. and Puade, O. A. eds. 2011 International Conference on Semantic Technology and Information Retrieval. IEEE, pp. 40-47. 10.1109/STAIR.2011.5995762 |
Abstract
The study presented in this paper focuses on the preprocessing stage of image retrieval by proposing an ontology-based indexing approach which captures the meaning of image annotations by extracting the semantic importance of the words in them. The indexing algorithm is based on the classic vector-space model that is adapted by employing index weighting and a word sense disambiguation. It uses sets of Semantic DNA, extracted from a lexical ontology, to represent the images in a vector space. As discussed in the paper, the use of Semantic DNA in text-based image retrieval aims to overcome some of the major drawbacks of well known traditional approaches such as `bags of words' and term frequency-(TF) based indexing. The proposed approach is evaluated by comparing the indexing achieved using the proposed semantic algorithm with results obtained using a traditional TF-based indexing in vector space model (VSM) with singular value decomposition (SVD) technique. The experimental results show that the proposed ontology-based approach generates a better-quality index which captures the conceptual meaning of the image annotations
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Uncontrolled Keywords: | image annotation ; semantic image indexing ; vector space model |
Publisher: | IEEE |
ISBN: | 9781612843537 |
Last Modified: | 06 Jul 2023 10:17 |
URI: | https://orca.cardiff.ac.uk/id/eprint/39107 |
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