PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semantic annotation of image groups with Self-Organizing Maps
Markus Koskela and Jorma Laaksonen
In: 4th International Conference on Image and Video Retrieval, 20-22 Jul 2005, Singapore.

Abstract

Automatic image annotation has attracted a lot of attention recently as a method for facilitating semantic indexing and text-based retrieval of visual content. In this paper, we propose the use of multiple Self-Organizing Maps in modeling various semantic concepts and annotating new input images automatically. The effect of the semantic gap is compensated by annotating multiple images concurrently, thus enabling more accurate estimation of the semantic concepts' distributions. The presented method is applied to annotating images from a freely-available database consisting of images of different semantic categories.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1729
Deposited By:Jorma Laaksonen
Deposited On:28 November 2005