Nowadays large volumes of data with high dimensionality are being generated in many fields. ClusterTree is a new indexing approach representing clusters generated by any existing clustering approach. It supports effective and efficient image retrieval. Lots of clustering algorithms have been developed, and in most of them some parameters should be determined by hand. The authors propose a new ClusterTree structure, which based on the improved CLIQUE and avoids any parameters defined by user. Using multi-resolution property of wavelet transforms, the proposed approach can cluster at different resolution and remain the relation between these clusters to construct hierarchical index. The results of the application confirm that the ClusterTree is very applicable and efficient. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Xu, H., Xu, D., & Lin, E. (2007). An applicable hierarchical clustering algorithm for content-based image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4418 LNCS, pp. 82–92). Springer Verlag. https://doi.org/10.1007/978-3-540-71457-6_8
Mendeley helps you to discover research relevant for your work.