Abstract
Abstract. Our objective is the categorization of the most dominant objects in facade images, like windows, entrances and balconies. In order to execute an image interpretation of complex scenes we need an interaction between low level bottom-up feature detection and highlevel inference from top-down. A top-down approach would use results of a bottom-up detection step as evidence for some high-level inference of scene interpretation. We present a statistically founded object categorization procedure that is suited for bottom-up object detection. Instead of choosing a bag of features in advance and learning models based on these features, it is more natural to learn which features best describe the target object classes. Therefore we learn increasingly complex aggregates of line junctions in image sections from man-made scenes. We present a method for the classification of image sections by using the histogram of diverse types of line aggregates.
Highlights
Our objective is the interpretation of facade images that leads to a detailed description including dominant objects such as windows, entrances and balconies
We believe that explicit modelling dominant facade objects gives much better evidence to guide a top-down interpretation system. In contrast to these approaches which deal with pixel wise evidence to guide top-down methods, we propose to learn generic parts of facade objects to allow object categorization from object specific image sections1, not whole scenes yet, see Figure 1 for some examples of given data
We proposed a method for classification using the histogram of types of relevant aggregates of straight line segments
Summary
Our objective is the interpretation of facade images that leads to a detailed description including dominant objects such as windows, entrances and balconies. This paper focuses on the categorization of objects in a facade, which is meant to serve a top-down module as a link to the image data. To motivate the idea of learning a feature representation for facade objects we will give a brief synopsis of recent work in the field of facade image interpretation. Their work can be seen in between window detection and exploiting repetitive structure. They propose a complex generative model in which they include object geometry as well as neighbourhood relations
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