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.