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

Abstract

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.