Content-Based Image Retrieval Using Multiple-Instance Learning

Abstract

We explore the application of machine learning techniques to the problem of content-based image retrieval (CBIR). Unlike most existing CBIR systems in which only global information is used or in which a user must explicitly indicate what part of the image is of interest, we apply the multiple-instance (MI) learning model to use a small number of training images to learn what images from the database are of interest to the user.