Boundary Detection Using F-Measure-, Filter- and Feature- (F3) Boost

  title={Boundary Detection Using F-Measure-, Filter- and Feature- (F3) Boost},
  author={Iasonas Kokkinos},
In this work we propose a boosting-based approach to boundary detection that advances the current state-of-the-art. To achieve this we introduce the following novel ideas: (a) we use a training criterion that approximates the F-measure of the classifier, instead of the exponential loss that is commonly used in boosting. We optimize this criterion using Anyboost. (b) We deal with the ambiguous information about orientation of the boundary in the annotation by treating it as a hidden variable… CONTINUE READING
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