In Content-based Image Retrieval the comparison of a query image and each of the database images is defined by a similarity distance obtained from the two feature vectors involved. These feature vectors can be seen as sets of noisy indexes. Unlike text matching (that is exact) image matching is only approximate, leading to ranking
methods. Only images at the top ranks (within the scope) are returned as retrieval results. Image retrieval performance characterization has mainly been based on measures available from probabilistic text retrieval in the form of Precision-Recall or Precision-Scope graphs. However, these graphs offer an incomplete overview of the image retrieval system under study. Essential information about how the success of the query is influenced by the size and type of irrelevant images is missing. Due to the inexactness of the visual matching process, the effect of the irrelevant embedding, represented in the additional performance measure generality, plays an important role.
In general, a performance graph will be three-dimensional, a Generality-Recall-Precision Graph. By choosing appropriate scope values a new two-dimensional performance graph, the Generality-Recall-Precision Graph, is proposed to replace the commonly used Precision-Recall Graph, as the better choice for total recall studies.