Learning to weight color and depth for RGB-D visual search

Both color and depth information may be deployed to seek by content through RGB-D imagery. Previous works dealing with global descriptors for RGB-D images advocate a decision level fusion whereby independently computed color and depth representations are juxtaposed to pursue similarity search. Differently, in this paper we propose a learning-to-rank paradigm aimed at weighting the two information channels according to the specific traits of the task and data at hand, thereby effortlessly addressing the potential diversity across applications. In particular, we propose a novel method, referred to as kNN-rank, which can learn the regularities among the outputs yielded by similarity-based queries. A further novel contribution of this paper concerns the HyperRGBD framework, a set of tools conceived to enable seamless aggregation of existing RGB-D datasets in order to obtain new data featuring desired peculiarities and cardinality.