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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Sequential Gaussian Mixture Models for Two-Level Conditional Random Fields
verfasst von : Sergey Kosov, Franz Rottensteiner, Christian Heipke
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
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Conditional Random Fields are among the most popular techniques for image labelling because of their flexibility in modelling dependencies between the labels and the image features. This paper addresses the problem of efficient classification of partially occluded objects. For this purpose we propose a novel Gaussian Mixture Model based on a sequential training procedure, in combination with multi-level CRF-framework. Our approach is evaluated on urban aerial images. It is shown to increase the classification accuracy in occluded areas by up to 14,4%.