Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.

► We propose a ranking framework for CBMIR using relevance feature mapping.
► We show that relevance features are more suitable for ranking than the original ones.
► Additional performance gain is achieved by incorporating the proposed ranking scheme.
► The framework uses no distance measure and scales up to large multimedia databases.