AG 2, AG 4   Info

MPI Audience

English

45 Minutes

Saarbrücken

The task of semantic scene interpretation is to label the regions of an image and their relations into meaningful classes. Such task is a key ingre- dient to many computer vision applications, including object recognition, 3D reconstruction and robotic perception. It is challenging partially due to the ambiguities inherent to the image data. The images of man-made scenes, e. g. the building facade images, exhibit strong contextual depen- dencies in the form of the spatial and hierarchical structures. Modeling these structures is central for such interpretation task. Graphical models provide a consistent framework for the statistical modeling. We develop a generic statistical graphical model for scene interpretation, which seamlessly integrates different types of the image features, and the spatial structural in- formation and the hierarchical structural information defined over the multi- scale image segmentation. It unifies the ideas of existing approaches, e. g. conditional random

field (CRF) and Bayesian network (BN), which has a clear statistical interpretation as the MAP estimate of a multi-class labeling problem. Given the graphical model structure, we derive the probability distribution of the model based on the factorization property implied in the model structure. The statistical model leads to an energy function that can be optimized approximately by either loopy belief propagation or graph cut based move making algorithm. We demonstrate the application of the pro- posed graphical model on the task of multi-class classification of building facade image regions.

Mario Fritz

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