Logistic regression model for relevance feedback in content-based image retrieval

We introduce logistic regression to model the dependence between image-features and the relevance that is implicitly defined by user-feedback. We assume that while browsing, the user can single out images as either examples or counter-examples of the sort of picture he is looking for. Based on this information, the system will construct logistic regression models that generalise this relevance probability to all images in the database. This information is then used to iteratively bias the next sample from the database. Furthermore, the diagnostics that are an integral part of the regression procedure can be harnessed for adaptive feature selection by removing features that have low predictive power.