Section: Scientific Foundations
Interactive search and personalisation
We are studying here the approaches that allow for a reduction of the "semantic gap" There are several ways to deal with the semantic gap. One prior work is to optimize the fidelity of physical-content descriptors (image signatures) to visual content appearance of the images. The objective of this preliminary step is to bridge what we call the numerical gap. To minimize the numerical gap, we have to develop efficient images signatures. The weakness of visual retrieval results, due to the numerical gap, is often confusingly attributed to the semantic gap. We think that providing richer user-system interaction allows user expression on his preferences and focus on his semantic visual-content target.
Rich user expression comes in a variety of forms :
allow the user to notify his satisfaction (or not) on the system retrieval results–method commonly called relevance feedback. In this case, the user reaction expresses more generally a subjective preference and therefore can compensate for the semantic gap between visual appearance and the user intention,
provide precise visual query formulation that allows the user to select precisely its region of interest and pull off the image parts that are not representative of his visual target,
provide a mechanism to search for the user mental image when no starting image example is available. Several approaches are investigated. As an example, we can mention the logical composition from visual thesaurus. Besides, learning methods related to information theory are also developed for efficient relevance feedback model in several context study including mental image retrieval.