Component-based visual clustering using the self-organizing map.

Mustaq Hussain; John P Eakins

doi:10.1016/j.neunet.2006.10.004

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Component-based visual clustering using the self-organizing map.

Mustaq Hussain·John P Eakins

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Abstract

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.

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Cluster Analysis Computer Graphics Database Management Systems Humans Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Information Storage and Retrieval Logical Observation Identifiers Names and Codes Pattern Recognition, Automated Reproducibility of Results Journal Article

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Mustaq Hussain, John P Eakins. 2006-12-21. Component-based visual clustering using the self-organizing map.. https://doi.org/10.1016/j.neunet.2006.10.004

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