Abstract
The aim of this research is to develop a system based on multi-attributes image analysis and a neural network self-organization feature map (SOFM) that will facilitate the automated classification of images or icons in Web documents. Four different image attribute sets are extracted. The system integrates different image attributes without demanding any particular primitive to be dominant. The system is implemented and the results generated show meaningful clusters. The performance of the system is compared with the Hierarchical Agglomerative Clustering (HAC) algorithm. Evaluation shows that similar images will fall onto the same region in our approach, in such a way that it is possible to retrieve images under family relationships.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chan, S.W.K. (2005). Multi-attributes Image Analysis for the Classification of Web Documents Using Unsupervised Technique. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_11
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DOI: https://doi.org/10.1007/11508069_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
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