An evaluation of retrieval effectiveness for a full-text document-retrieval system
Communications of the ACM
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International Journal of Computer Vision
A society of models for video and image libraries
IBM Systems Journal
Trademark shape recognition using closed contours
Pattern Recognition Letters
GTM: the generative topographic mapping
Neural Computation
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Introduction to Modern Information Retrieval
Similarity Retrieval of Trademark Images
IEEE MultiMedia
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Visual Clustering of Trademarks Using the Self-Organizing Map
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ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Multi-modal Retrieval of Trademark Images Using Global Similarity TITLE2:
Multi-modal Retrieval of Trademark Images Using Global Similarity TITLE2:
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IEEE Transactions on Computers
Shape feature matching for trademark image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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IEEE Transactions on Circuits and Systems for Video Technology
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
A Fuzzy Approach of the Kohonen's Maps Applied to the Analysis of Biomedical Signals
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
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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.