An applicable hierarchical clustering algorithm for content-based image retrieval

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.