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

Iasonas Kokkinos
2010 Lecture Notes in Computer Science  
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,
more » ... train our classifier using Multiple-Instance Learning. (c) We adapt the Filterboost approach of [1] to leverage information from the whole training set to train our classifier, instead of using a fixed subset of points. (d) We extract discriminative features from appearance descriptors that are computed densely over the image. We demonstrate the performance of our approach on the Berkeley Segmentation Benchmark.
doi:10.1007/978-3-642-15552-9_47 fatcat:ntj6w7b3vfe45b7c4awk4a25uy