More precisely, we have conducted a number of subjective experiments in which we asked human subjects to group images, and then explain verbally why they did so. The results of this study indicated that a part of the abstraction involved in image interpretation is often driven by semantic categories, which can be broken into more tangible semantic entities, i.e. objective semantic indicators. By analyzing our experimental data, we have identified some candidate semantic categories (i.e. portraits, people, crowds, cityscapes, landscapes, etc.) and their underlying semantic indicators (i.e. skin, sky, water, object, etc.). These experiments also helped us derive important low-level image descriptors, accounting for our perception of these indicators.

We have then used these findings to develop an image feature extraction and indexing scheme. In particular, our feature set has been carefully designed to match the way humans communicate image meaning. This led us to the development of a semantic-friendly query language for browsing and searching diverse collections of images.

We have implemented our approach into an Internet search engine, and tested it on a large number of images. The results we obtained are very promising.