In this article we present research work in the field of content-based image retrieval in large databases applied to the paleontology image database of the Université de Bourgogne, Dijon, France, called "TRANS'TYFIPAL". Our indexing method is based on multiresolution decomposition of database images using wavelets. For each family of paleontology images we try to find a model image that represents it. The K-means automatic classification algorithm divides the space of parameters into several clusters. A model image for each cluster is computed from the wavelet transform of each image of the cluster. Then a search tree is built to offer users a graphic interface for retrieving images. So users have to navigate through this tree of model images to find an image similar to that they are requesting. Our contribution in the field is the building of the model and of the search tree to make user access easier and faster. At the end of this article we give experimental results and a description of future work that will be done to enhance our indexing and retrieval method.