Semantic Segmentation of Street-Side Images
Michal Récky, Franz W. Leberl · 2009
In this paper we propose a method for semantic segmentation of street-side images. Segmentation and classification is pixel based and results in classes of building facades, sections of sky, road and other areas present in general images taken in the urban environment. A segmentation method is suggested and detected segments are classified. Final classification is reinforced using context information implemented in a form of the discriminative random fields (DRF). Results show that this approach can overcome problems with the lack of features, as additional constraints are used in the classification.