How to complete performance graphs in content-based image retrieval: add generality and normalize scope

The performance of a content-based image retrieval (CBIR) system, presented in the form of precision-recall or precision-scope graphs, offers an incomplete overview of the system under study: the influence of the irrelevant items (embedding) is obscured. We propose a comprehensive and well-normalized description of the ranking performance compared to the performance of an ideal retrieval system defined by ground-truth for a large number of predefined queries. We advocate normalization with respect to relevant class size and restriction to specific normalized scope values (the number of retrieved items). We also propose new three and two-dimensional performance graphs for total recall studies in a range of embeddings.