Abstract
We propose a combination of multiple Conditional Random Field (CRF) models with a linear classifier. The model is used for the semantic labeling of 3-D surface meshes with large variability in shape. The model employs multiple CRFs of low complexity for surface labeling each of which models the distribution of labelings for a group of surfaces with a similar shape. Given a test surface the classifier exploits the MAP energies of the inferred CRF labelings to determine the shape class. We discuss the associated recognition and learning tasks and demonstrate the capability of the joint shape classification and labeling model on the object category of human outer ears.
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Zouhar, A., Schlesinger, D., Fuchs, S. (2013). Joint Shape Classification and Labeling of 3-D Objects Using the Energy Minimization Framework. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_8
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DOI: https://doi.org/10.1007/978-3-642-40602-7_8
Publisher Name: Springer, Berlin, Heidelberg
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