TY  - CONF
AV  - none
CY  - Istanbul
SP  - 414
ID  - scholars2222
TI  - Adding semantics to the reliable object annotated image databases
KW  - Data sets; Image database; Information Extraction; Knowledge base; Manual annotation; Multimedia annotation; Multimedia information; Multimedia search; Retrieval performance; Semantic content; Semantic enrichment; Semantic gap; Semantic intensity; Semantic interpretation; Semantic similarity; Wordnet
KW  -  Information retrieval systems
KW  -  Semantics
N2  - Semantically enriched multimedia information is crucial for equipping the kind of multimedia search potentials that professional searchers need. But the semantic interpretation of multimedia is obsolete without some mechanism for understanding semantic content that is not explicitly available. Manual annotation is the only source to overwhelming this, which is not only time consuming and costly but also lacks semantic enrichment in terms of concept diversity and concept enrichability. In this paper, we present semantically enhanced information extraction model that calculate the semantic intensity (SI) of each object in the image and then enhance the tagged concept with the assistance of lexical and conceptual knowledgebases. i.e. WordNet and ConceptNet. Noises, redundant and unusual words are then filtered out by means of various techniques like semantic similarity, stopwords and words unification. The experiment has been carried out on the LabelMe datasets. Results demonstrate the substantial improvement in terms of concept diversity, concept enrichment and retrieval performance. © 2010 Published by Elsevier Ltd.
N1  - cited By 6; Conference of 1st World Conference on Information Technology, WCIT-2010 ; Conference Date: 6 October 2010 Through 10 October 2010; Conference Code:84172
SN  - 18770509
Y1  - 2011///
EP  - 419
VL  - 3
A1  - Irfanullah
A1  - Aslam, N.
A1  - Loo, J.
A1  - Roohullah
A1  - Loomes, M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952514985&doi=10.1016%2fj.procs.2010.12.069&partnerID=40&md5=c4157321d77649f6cedbe2713311ee0e
ER  -