Statistical score fusion for 3D object retrieval

In this work, we introduce the score fusion problem for 3D object retrieval. Ongoing research in 3D object retrieval shows that no single descriptor is capable of providing fine grain discrimination required by prospective 3D search engines. We present a fusion algorithm that linearly combines similarity information originating from multiple shape descriptors. We learn the optimal set of weights in the linear combination by minimizing the emprical ranking risk. The algorithm is based on a recently introduced rigorous statistical ranking framework, for which consistency and fast rate of convergence of empirical ranking risk minimizers have been established. We report the results of relevance feedback search on a large 3D object database, the Princeton Shape Benchmark. Experiments show that, under query formulations with user intervention, the proposed score fusion scheme boosts the performance of the 3D retrieval machine significantly.