Comparison and combination of adaptive query shifting and feature relevance learning for content-based image retrieval

Despite the efforts to reduce the semantic gap between user perception of similarity and feature-based representation of images, user interaction is essential to improve retrieval performance in content-based image retrieval. To this end a number of relevance feedback mechanisms are currently adopted to refine image queries. They are aimed either to locally modify the feature space or to shift the query point towards more promising regions of the feature space. A novel adaptive query shifting mechanism is proposed to improve retrieval performance beyond that provided by other relevance feedback mechanisms. In addition we discuss the extent to which query shifting may provide better performance than feature weighting and provide experimental results on the complementarity of the two approaches. Finally, some combinational approaches are proposed to exploit such complementarities.