Query-based occupancy map for SVM-CBIR on mobile robot image database

A set of images taken by a mobile robot during navigation can be viewed as a large-scale image database, content-based image retrieval (CBIR) is an effective approach to image retrieval on such large-scale image databases. For the aim of human-robot interface, CBIR on the robot image database would be very useful, to train the robot to learn features (e.g. color, texture) of the relevant objects (e.g. book, chair) that a human user is interested in. To achieve CBIR, a small but sufficient member of relevant objects´ features are required. We will propose a novel CBIR method that efficiently searches such relevant objects based not only on their features, but also on their locations with respect to the robot´s workspace. To combine both of the feature and the location information, a novel 2D occupancy map called query based occupancy map (QOM) is presented.