Mobile Museum Guidance Using Relational Multi-Image Classification
Erich Bruns, Oliver Bimber · 2010
In this paper we present a multi-image classification technique for mobile phones that is supported by relational reasoning. Users capture a sequence of images employing a simple near-far camera movement. After classifying distinct keyframes using a nearest-neighbor approach the corresponding database images are only considered for a majority voting if they exhibit similar near-far inter-image relations to the captured keyframes. In the context of PhoneGuide, our adaptive mobile museum guidance system, a user study revealed that our multi-image classification technique leads to significantly higher classification rates than single image classification. Furthermore, when using near-far image relations, less keyframes are sufficient for classification. This increases the overall classification speed of our approach by up to 35%.