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part of the problem, keeping an accurate overview of one’s visual asset is far from trivial. However, as discussed towards the end of this text, on the way to solving this part, one finds interesting visual properties and features that go beyond the technicalities of developing such a system.
Most current Multimedia Information Management frameworks now are query-based. That is, they mostly rely on the assumption that the user is looking for something and has a good idea of what (s)he is looking for. This can be mapped onto the concept of Query-by-Example, where the user is able to produce an example of what (s)he is looking for. Browsing is another concept for searching information. It also assumes that the users hold the definition of a specific target. In both concepts, the user should be able to produce a query for the information needed.
Following the classical phrase that «an image is worth a thousand words,» the query-by-visual-example (QBE) paradigm simply wishes to avoid the tedious and imprecise textual description of the wanted item.
When looking for an image, the user of such a QBE system is asked to produce one or more positive or negative image examples to the system to express features that are desired. Clearly, the underlying goal is to capture content semantics to reach an accurate level of retrieval. However, recent studies have converged towards the use of relatively basic visual content characteristics. Most used features are
• Color, characterized by numerical values or an index in a palette;
• Texture, capturing the regular pattern of the visual content at hand. Numerical values associated to it may be the dominant orientation or some measure of coarseness;
• Shape, encoding the composition of recognizable objects. Geometrical values such as area and perimeter may be used here. Clearly these aspects characterize visual content but no or little semantics. To reach more visual properties, system designers have moved towards specific classification such as
