title: Interactive image data labeling using self-organizing maps in an augmented reality scenario.
abstract: We present an approach for the convenient labeling of image patches gathered from an unrestricted environment. The system is employed for a mobile Augmented Reality (AR) gear: while the user walks around with the head-mounted AR-gear, context-free modules for focus-of-attention permanently sample the most 'interesting' image patches. After this acquisition phase, a Self-Organizing Map (SOM) is trained on the complete set of patches, using combinations of MPEG-7 features as a data representation. The SOM allows visualization of the sampled patches and an easy manual sorting into categories. With very little effort, the user can compose a training set for a classifier, thus, unknown objects can be made known to the system. We evaluate the system for COIL-imagery and demonstrate that a user can reach satisfying categorization within few steps, even for image data sampled from walking in an office environment. (An abbreviated version of some portions of this article appeared in [Bekel, H., Heidemann, G., & Ritter, H. (2005). SOM Based Image Data Structuring in an Augmented Reality Scenario. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada.], as part of the IJCNN 2005 conference proceedings, published under the IEEE copyright).
authors.affiliation: Neuroinformatics Group, Bielefeld University, P.O. Box 10 01 31, D-33501 Bielefeld, Germany. hbekel@techfak.uni-bielefeld.de
authors.forename: Holger
authors.initials: H
authors.lastname: Bekel
authors.forename: Gunther
authors.initials: G
authors.lastname: Heidemann
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
mesh_terms: D000465:Algorithms; D003116:Color; D003196:Computer Graphics; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016247:Information Storage and Retrieval; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D014796:Visual Perception