Indoor location recognition using fusion of SVM-based visual classifiers

Abstract

We apply our general-purpose algorithm for visual category recognition using bag-of-visual-words and other visual features and fusion of SVM classifiers to the recognition of indoor locations. This is an important application in many emerging fields, such as mobile augmented reality and autonomous robots. We evaluate the proposed method with other location recognition systems in the ImageCLEF 2010 RobotVision contest. The results show that given a large enough training set, a purely appearance-based method can perform very well - ranked first for one of the contest's training sets.