Unsupervised Facade Segmentation using Repetitive Patterns

Abstract. We introduce a novel approach for separating and segmenting individual facades from streetside images. Our algorithm incorporates prior knowledge about arbitrarily shaped repetitive regions which are detected using intensity profile descriptors and a voting–based matcher. In the experiments we compare our approach to extended state–of–the–art matching approaches using more than 600 challenging streetside images, including different building styles and various occlusions. Our algorithm outperforms these approaches and allows to correctly separate 94 % of the facades. Pixel–wise comparison to our ground–truth yields a segmentation accuracy of 85%. According to these results our work is an important contribution to fully automatic building reconstruction.

Fault-Tolerant Concept Detection in Information Networks

Given information about medical drugs and their properties, how can we automatically discover that Aspirin has blood-thinning properties, and thus prevents heart attacks? Expressed in more general terms, if we have a large in- formation network that integrates data from heterogeneous data sources, how can we extract semantic information that provides a better understanding of the integrated data and also helps us to identify missing links? We propose to extract concepts that describe groups of objects and their common properties from the integrated data. The discovered concepts provide semantic information as well as an abstract view on the integrated data and thus improve the understanding of complex systems. Our proposed method has the following desirable properties: (a) it is parameter-free and therefore requires no user-defined parameters (b) it is fault-tolerant, allowing for the detection of missing links and (c) it is scalable, being linear on the input size. We demonstrate the effectiveness and scalability of the proposed method on real, publicly available graphs