Pareto-depth for Multiple-query Image Retrieval
This addresses the challenge of retrieving images based on diverse semantic queries, which is incremental as it builds on existing multiple-query retrieval methods.
The paper tackles the problem of content-based image retrieval with multiple query images representing different semantics, proposing a novel algorithm that combines Pareto front method with efficient manifold ranking. The result shows that this algorithm outperforms state-of-the-art methods on real-world image databases, with performance improvements attributed to concavity properties of Pareto fronts.
Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method (PFM) with efficient manifold ranking (EMR). We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.