Mean Normalized Retrieval Order (MNRO): a new content-based image retrieval performance measure

Abstract

The results of a content based image retrieval system can be evaluated by several performance measures, each one employing different evaluation criteria. Many of the methods used in the field of information retrieval have been adopted for use in image retrieval systems. This paper reviews the most widely used performance measures for retrieval evaluation with particular emphasis on the assumptions made during their design. More specifically, it focuses on the design principles of the commonly used Mean Average Precision (MAP) and Average Normalized Modified Retrieval Rank (ANMRR), pinpointing their limitations. It also proposes a new performance measure for image retrieval systems, the Mean Normalized Retrieval Order (MNRO), whose effectiveness is demonstrated through a wide range of experiments. Initial experiments were conducted on artificially produced query trials and evaluations. Experiments on a large database demonstrate the ability of MNRO to take into account the generality of the queries during the retrieval procedure. Furthermore, the results of a case study show that the proposed performance measure is closer to human evaluations, in comparison to MAP and ANMRR. Lastly, in order to encourage researchers and practitioners to use the proposed performance measure, we present the experimental results produced by a large number of state of the art descriptors applied on three well-known benchmarking databases.