A study on the evaluation of relevance feedback in multi-tagged image datasets

This paper proposes a study on the evaluation of relevance feedback approaches when a multi-tagged dataset is available. The aim of this study is to verify how the relevance feedback works in a real-word scenario, i.e. by taking into account the multiple concepts represented by the query image. To this end, we first assessed how relevance feedback mechanisms adapt the search when the same image is used for retrieving different concepts. Then, we investigated the scenarios in which the same image is used for retrieving multiple concepts. The experimental results shows that relevance feedback can effectively focus the search according to the user's feedback even if the query image provides a rough example of the target concept. We also propose two performance measures aimed at comparing the accuracy of retrieval results when the same image is used as a prototype for a number of different concepts.