Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Ontology-based indexing of annotated images using semantic DNA and vector space model

Fadzli, S. A. and Setchi, Rossitza 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

Full text not available from this repository.

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)
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: 04 Jun 2017 04:26
URI: http://orca.cf.ac.uk/id/eprint/39107

Citation Data

Cited 1 time in Google Scholar. View in Google Scholar

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item