Abstract
Traditional probabilistic relaxation, as proposed by Rosenfeld, Hummel and Zucker, uses a support function which is a double sum over neighboring nodes and labels. Recently, Pelillo has shown the relevance of the Baum-Eagon theorem to the traditional formulation. Traditional probabilistic relaxation is now well understood in an optimization framework. Kittler and Hancock have suggested a form of probabilistic relaxation with product support, based on an evidence combining formula. In this paper we present a formal basis for Kittler and Hancocks probabilistic relaxation. We show that it too has close links with the Baum-Eagon theorem, and may be understood in an optimization framework. We provide some proofs to show that a stable stationary point must be a local maximum of an objective function. We present a new form of probabilistic relaxation that can be used as an approximate maximizer of the global labeling with maximum posterior probability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.