Context Modeling in CBIR

The notion of context is a key for problem solving in many domains, especially in Content-Based Image Retrieval (CBIR). However, one often considers the word context like words “concept” or “system”, i.e. without giving a clear definition. This problem has been encountered in artificial intelligence and fixed with a context model and a software called contextual graphs. In this paper, we point out that there are several types of context in CBIR. Thus, if one wish to use efficiently context, we need before to identify and model it correctly. We show then that it is possible to improve the different steps in the CBIR processing. We illustrate this point on two steps, namely the user’s query management and the disease diagnosis from images, thanks to methods and tools coming from artificial intelligence.