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. In this paper, 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).