Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors

We present a novel approach for fast object class recognition incorporating contextual information into boosting. The object is represented as a constellation of generalized correlograms that integrate both information of local parts and their spatial relations. Incorporating the spatial relations into our constellation of descriptors, we show that an exhaustive search for the best matching can be avoided. Combining the contextual descriptors with boosting, the system simultaneously learns the information that characterize each part of the object along with their characteristic mutual spatial relations. The proposed framework includes a matching step between homologous parts in the training set, and learning the spatial pattern after matching. In the matching part two approaches are provided: a supervised algorithm and an unsupervised one. Our results are favorably compared against state-of-the-art results.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Jaume Amores, Nicu Sebe, Petia Radeva
• Keywords: Computer Vision, Contextual Descriptors, CVPR 2005, Mutual Spatial Relations, Object Class Recognition, Spatial Pattern, Spatial Relations