Affordable Access

Publisher Website

Component-based visual clustering using the self-organizing map

Authors
Journal
Neural Networks
0893-6080
Publisher
Elsevier
Publication Date
Volume
20
Issue
2
Identifiers
DOI: 10.1016/j.neunet.2006.10.004
Keywords
  • Component Matching
  • Content-Based Image Retrieval
  • Feature Extraction
  • Image Segmentation
  • Self-Organizing Map
  • Similarity Measures
  • Trademark Image Retrieval
Disciplines
  • Mathematics

Abstract

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.

There are no comments yet on this publication. Be the first to share your thoughts.