Component-based visual clustering using the self-organizing map.
In this paper we present a new method for visual clustering of multi-component images such as trademarks, using the topological properties of the self-organizing map, and show how it can be used for similarity retrieval from a database. The method involves two stages: firstly, the construction of a 2D map based on features extracted from image components, and secondly the derivation of a Component Similarity Vector from a query image, which is used in turn to derive a 2D map of retrieved images. The retrieval effectiveness of this novel component-based shape matching approach has been evaluated on a set of over 10 000 trademark images, using a spatially-based precision-recall measure. Our results suggest that our component-based matching technique performs markedly better than matching using whole-image clustering, and is relatively insensitive to changes in input parameters such as network size.
Hussain
Mustaq
M
School of Informatics, University of Northumbria at Newcastle, NE1 8ST, United Kingdom. mustaq.hussain@unn.ac.uk
Eakins
John P
JP
D016000:Cluster Analysis; D003196:Computer Graphics; D003628:Database Management Systems; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D016247:Information Storage and Retrieval; D039703:Logical Observation Identifiers Names and Codes; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results