We present a new system, called Retimm, for searching databases made of documents containing images and text. Images are indexed by color and texture distributions. Color and texture classes are obtained by a quantization adapted to the whole database. Signatures are ranked m times, once for each dimension, but values are not stored. The search engine works as a vote system: the score for each document is the total of the votes of all coordinates, these last votes depending on a k-nn search on each dimension. Retimm is able to retrieve very quickly images from large databases from any request composed of one or several images and/or one or several words. The system is interactive, since the query can be modified at any moment by adding or removing images or words

This paper presents a new technique for fast, view-dependent, real-time visualization of large multiresolution geometric models with color or texture information. This method uses geomorphing to smoothly interpolate between geometric patches composing a hierarchical level-of-detail structure, and to maintain seamless continuity between neighboring patches of the model. It combines the advantages of view-dependent rendering with numerous additional features: the high performance rendering associated with static preoptimized geometry, the capability to display at both low and high resolution with minimal artefacts, and a low CPU usage since all the geomorphing is done on the GPU. Furthermore, the hierarchical subdivision of the model into a tree structure can be accomplished according to any spatial or topological criteria. This property is particularly useful in dealing with models with high resolution textures derived from digital photographs. Results are presented for both highly tesselated models (372 million triangles), and for models which also contain large quantities of texture (200 million triangles + 20 GB of compressed texture). The method also incorporates asynchronous out-of-core model management. Performances obtained on commodity hardware are in the range of 50 million geomorphed triangles/second for a benchmark model such as Stanford's St. Matthew dataset.

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.

Over the last 20 years, several efforts have been devoted to the application of image processing and digital imaging techniques to art research in a context of close collaboration between the Centre de Recherche et de Restauration des Muse/spl acute/es de France (C2RMF) and the Ecole Nationale Superieure des Telecommunications (ENST). These efforts often found developments within National or European projects and were only made possible by the strong support of many partners throughout Europe. This set of projects and some of its results are presented, covering acquisition of information (from photos, or directly from paintings or objects), as well as storage management, database consulting, and image processing for pattern recognition and feature extraction.