Adaptable Similarity Search using Non-Relevant Information

Many modern database applications require content-based similarity search capability in numeric attribute space. Further, users’ notion of similarity varies between search sessions. Therefore online techniques for adaptively refining the similarity metric based on relevance feedback from the user are necessary. Existing methods use retrieved items marked relevant by the user to refine the similarity metric, without taking into account the information about non-relevant (or unsatisfactory) items. Consequently items in database close to non-relevant ones continue to be retrieved in further iterations. In this paper a robust technique is proposed to incorporate non-relevant information to efficiently discover the feasible search region. A decision surface is determined to split the attribute space into relevant and non-relevant regions. The decision surface is composed of hyperplanes, each of which is normal to the minimum distance vector from a nonrelevant point to the convex hull of the relevant points. A similarity metric, estimated using the relevant objects is used to rank and retrieve database objects in the relevant region. Experiments on simulated and benchmark datasets demonstrate robustness and superior performance of the proposed technique over existing adaptive similarity search techniques.