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Abstract image semantics resists all forms of modeling, very much like any kind of intelligence does. However, in order to develop more satisfying image navigation systems, we need tools to construct a semantic bridge between the user and the database. In this paper we present an image indexing scheme and a query language, which allow the user to introduce cognitive dimension to the search. At an abstract level, this approach consists of: (1) learning the “natural language” that humans speak to communicate their semantic experience of images, (2) understanding the relationships between this language and objective measurable image attributes, and then (3) developing corresponding feature extraction schemes.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.