Similarity learning for 3D object retrieval using relevance feedback and risk minimization

Abstract

We introduce a similarity learning scheme to improve the 3D object retrieval performance in a relevance feedback setting. The proposed algorithm relies on a score fusion approach that linearly combines elementary similarity scores originating from different shape descriptors into a final similarity function. Each elementary score is modeled in terms of the posterior probability of a database item being relevant to the user-provided query. The posterior parameters are learned via off-line discriminative training, while the optimal combination of weights to generate the final similarity function is obtained by on-line empirical ranking risk minimization. This joint use of on-line and off-line learning methods in relevance feedback not only improves the retrieval performance significantly as compared to the totally unsupervised case, but also outperforms the standard support vector machines based approach. Experiments on several 3D databases, including the Princeton Shape Benchmark, show also that the proposed algorithm has a better small sample behavior.