Section: Overall Objectives
One of the consequences of the increasing ease of use and significant cost reduction of computer systems is the production and exchange of more and more digital and multimedia documents. These documents are fundamentally heterogeneous in structure and content as they usually contain text, images, graphics, video and sounds.
Information retrieval can no longer rely on text-based queries alone; it will have to be multi-modal and to integrate all the aspects of the multimedia content. In particular, the visual content has a major role and represents a central vector for the transmission of information. The description of that content by means of image analysis techniques is less subjective than the usual keyword-based annotations, whenever they exist. Moreover, being independent from the query language, the description of visual content is becoming paramount for the efficient exploration of a multimedia stream.
In the IMEDIA group we focus on the intelligent access by visual content. With this goal in mind, we develop methods that address key issues such as content-based indexing, interactive search and image database navigation, in the context of multimedia content.
Content-based image retrieval systems provide help for the automatic search and assist human decisions. The user remains the maître d'oeuvre , the only one able to take the final decision. The numerous research activities in this field during the last decade have proved that retrieval based on the visual content was feasible. Nevertheless, current practice shows that a usability gap remains between the designers of these techniques/methods and their potential users.
One of the main goals of our research group is to reduce the gap between the real usages and the functionalities resulting from our research on visual content-based information retrieval. Thus, we apply ourselves to conceive methods and techniques that can address realistic scenarios, which often lead to exciting methodological challenges.
Among the "usage" objectives, an important one is the ability, for the user, to express his specific visual interest for a part of a picture. It allows him to better target his intention and to formulate it more accurately. Another goal in the same spirit is to express subjective preferences and to provide the system with the ability to learn those preferences. When dealing with any of these issues, we keep in mind the importance of the scalability of such interactive systems in terms of indexing and response times. Of course, what value these times should have and how critical they are depend heavily on the domain (specific or generic) and on the cost of the errors.
Our research work is then at the intersection of several scientific specialities. The main ones are image analysis, pattern recognition, statistical learning, human-machine interaction and database systems. It is structured into the following main themes:
Image indexing: this part mainly concerns modeling the visual aspect of images, by means of image analysis techniques. It leads to the design of image signatures that can then be obtained automatically.
Clustering and statistical learning: generic and fundamental methods for solving problems of pattern recognition, which are central in the context of image indexing.
Interactive search and personalization: to let the system take into account the preferences of the user, who usually expresses subjective or high-level semantic queries.
Cross-media indexing, and in particular bimodal text + image indexing, which addresses the challenge of combining those two media for a more efficient indexing and retrieval.
More generally, the research work and the academic and industrial collaborations of the IMEDIA team aim to answer the complex problem of the intelligent access to multimedia content.