PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Emergence of semantic concepts in visual databases
Jorma Laaksonen, Ville Viitaniemi and Markus Koskela
In: International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, 15-17 Jun 2005, Espoo, Finland.

Abstract

Content-based image retrieval (CBIR) systems can be used also for other purposes than online access to unannotated image databases. In particular, when a CBIR system is equipped with an automatic image segmentation subsystem, keyword annotations given on image level can be focused on specific image segments. In this paper, we show that our PicSOM CBIR system is able to reveal semantic knowledge not only from keyword annotations but also from recorded online use of the system. This automatically extracted high abstraction level visual information can then be used to further improve the accuracy of the system and to categorize the objects of the database with semantic concepts. This process, we claim, then helps to bridge the semantic gap between low-level visual features available for computers and the high-level semantic terms used by the humans. The results of the experiments described in this paper support that view.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Multimodal Integration
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1726
Deposited By:Jorma Laaksonen
Deposited On:28 November 2005