Relevance Feedback Models for Content-Based Image Retrieval

We investigate models for content-based image retrieval with relevance feedback, in particular focusing on the exploration-exploitation dilemma. We propose quantitative models for the user behavior and investigate implications of these models. Three search algorithms for efficient searches based on the user models are proposed and evaluated. In the first model a user queries a database for the most (or a sufficiently) relevant image. The user gives feedback to the system by selecting the most relevant image from a number of images presented by the system. In the second model we consider a filtering task where relevant images should be extracted from a database and presented to the user. The feedback of the user is a binary classification of each presented image as relevant or irrelevant. While these models are related, they differ significantly in the kind of feedback provided by the user. This requires very different mechanisms to trade off exploration (finding out what the user wants) and exploitation (serving images which the system believes relevant for the user).