PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Measuring concept similarities in multimedia ontologies: Analysis and evaluations
Markus Koskela, Alan F. Smeaton and Jorma Laaksonen
IEEE Transactions on Multimedia, Volume 9, Number 5, pp. 912-922, 2007.

Abstract

The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
Multimodal Integration
Information Retrieval & Textual Information Access
ID Code:3636
Deposited By:Jorma Laaksonen
Deposited On:14 February 2008