Séminaire du jeudi 27 janvier 2011
Orateur: Yuri Bokov
Titre: Energy Minimization with Label costs and Applications in Multi-Model
Fitting
Résumé: The a-expansion algorithm has had a significant impact in computer
vision due to its generality, effectiveness, and speed. Until recently, it
could only minimize energies that involve unary, pairwise, and specialized
higher-order terms. We propose an extension of a-expansion that can
simultaneously optimize ``label costs'' with certain optimality guarantees.
An energy with label costs can penalize a solution based on the set of
labels that appear in it. The simplest special case is to penalize the
number of labels in the solution, but the proposed energy is significantly
more general than this. Usefulness of label costs is demonstrated by a
number of specific applications in vision that appeared in the last year.
Our work (CVPR 2010, IJCV accepted) studies label costs from a general
perspective, including discussion of multiple algorithms, optimality bounds,
extensions, and fast special cases (e.g. UFL heuristics). In this talk we
focus on natural generic applications of label costs is multi-model fitting
and demonstrate several examples: homography detection, motion segmentation,
unsupervised image segmentation, compression, and FMM. We also discuss a
method for effective exploration of the continuum of labels - an important
practical obstacle for a-expansion in model fitting. We discuss why our
optimization-based approach to multi-model fitting is significantly more
robust than standard extensions of RANSAC (e.g. sequential RANSAC) currently
dominant in vision.
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CMLA UMR 8536 |
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