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 precisionrecall 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.
self-organizing map feature extraction component matching content-based image retrieval image segmentation similarity measures trademark image retrieval