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Séminaire Vision artificielle / Équipe Willow

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Building local part models for category-level recognition
Cordelia Schmid (INRIA)

6 octobre 2005

This talk addresses the problem of building semi-local part models for category-level recognition. In the context of category recognition, it is no longer sufficient to use individual local features, and it becomes necessary to model intra-class variations, to select discriminant features, and to model spatial relations. This leads to a part-based approach to category-level recognition that I will illustrate with two examples. The first one represents images as distributions of local parts and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the Chi-square distance. The second one represents object classes with a dictionary of composite semi-local parts, i.e., groups of neighboring keypoints with stable and distinctive appearance and geometric layout. A discriminative maximum entropy framework is used to learn the posterior distribution of the class label given the occurrences of parts from the dictionary in the training set.
This is joint work with S. Lazebnik, M. Marszalek, J. Zhang and J. Ponce.

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Cordelia Schmid Cordelia Schmid (INRIA)
LEAR Project, INRIA Rhone-Alpes