Hierarchical architecture for content-based image retrieval of paleontology images

In this article a research work in the field of content-based multiresolution indexing and retrieval of images is presented. Our method uses multiresolution decomposition of images using wavelets in the HSV colorspace to extract parameters at multiple scales allowing a progressive (coarse-to-fine) retrieval process. Features are automatically classified into several clusters with K-means algorithm. A model image is computed for each cluster in order to represent all the images of this cluster. The process is reiterated again and again and each cluster is sub-divided into sub-clusters. The model images are stored in a tree which is proposed to users for browsing the database. The nodes of the tree are the families and the leaves are the images of the database. A paleontology images database is used to test the proposed technique. This kind of approach permits to build a visual interface easy to use for users. Our main contribution is the building of the tree with multiresolution indexing and retrieval of images and the generation of model images to be proposed to users.