In this work, we present a new type of model for object localization, which is well suited for anatomical objects
exhibiting large variability in size, shape and posture, for usage in the discriminative generalized Hough transform
(DGHT). The DGHT combines the generalized Hough transform (GHT) with a discriminative training approach
to automatically obtain robust and efficient models. It has been shown to be a strong tool for object localization
capable of handling a rather large amount of shape variability. For some tasks, however, the variability exhibited
by different occurrences of the target object becomes too large to be represented by a standard DGHT model. To
be able to capture such highly variable objects, several sub-models, representing the modes of variability as seen by
the DGHT, are created automatically and are arranged in a higher dimensional model. The modes of variability
are identified on-the-fly during training in an unsupervised manner. Following the concept of the DGHT, the
sub-models are jointly trained with respect to a minimal localization error employing the discriminative training
approach. The procedure is tested on a dataset of thorax radiographs with the target to localize the clavicles.
Due to different arm positions, the posture and arrangement of the target and surrounding bones differs strongly,
which hampers the training of a good localization model. Employing the new model approach the localization
rate improves by 13% on unseen test data compared to the standard model.