Abstract
This paper presents a novel part-based geometry model for 3D object classes based on latent Dirichlet allocation (LDA). With all object instances of the same category aligned to a canonical pose, the bounding box is discretized to form a 3D space dictionary for LDA. To enhance the spatial coherence of each part during model learning, we extend LDA by strategically constructing a Markov random field (MRF) on the part labels, and adding an extra spatial parameter for each part. We refer to the improved model as spatial latent Dirichlet Markov random fields (SLDMRF). The experimental results demonstrate that SLDMRF exhibits superior semantic interpretation and discriminative ability in model classification to LDA and other related models.
The research has received funding from the European Community’s Seventh Framework Programme (FP7) under grant agreement no. 270273, Xperience.
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Xiong, H., Szedmak, S., Piater, J. (2013). 3D Object Class Geometry Modeling with Spatial Latent Dirichlet Markov Random Fields. 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_6
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DOI: https://doi.org/10.1007/978-3-642-40602-7_6
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