Performance Evaluation of Relevance Feedback for Image Retrieval by “Real-World” Multi-Tagged Image Datasets

Anyone who has ever tried to describe a picture in words is aware that it is not an easy task to find a word, a concept, or a category that characterizes it completely. Most images in real life represent more than a concept; therefore, it is natural that images available to users over the Internet (e.g., FLICKR) are associated with multiple tags. By the term ‘tag’, the authors refer to a concept represented in the image. The purpose of this paper is to evaluate the performances of relevance feedback techniques in content-based image retrieval scenarios with multi-tag datasets, as typically performances are assessed on single-tag dataset. Thus, the authors show how relevance feedback mechanisms are able to adapt the search to user’s needs either in the case an image is used as an example for retrieving images each bearing different concepts, or the sample image is used to retrieve images containing the same set of concepts. In this paper, the authors also propose two novel performance measures aimed at comparing the accuracy of retrieval results when an image is used as a prototype for a number of different concepts.