Query Reformulation for Content Based Multimedia Retrieval in MARS

Unlike traditional database management systems, in content-based multimedia retrieval databases, it is difficult for users to express their exact information need directly in a precise query. A typical interface allows users to express their information need by using examples of objects similar to the ones they wish to retrieve. Such a user interface, however, requires mechanisms to learn the query representation from the examples. In this paper, we describe the query refinement framework implemented in the Multimedia Analysis and Retrieval System (MARS) for learning query representations using relevance feedback. The proposed framework uses a query expansion approach towards modifying the query representation in which relevant objects are added to the query. Furthermore, query reweighting techniques are used to adjust similarity functions.