Structured Local Predictors for image labelling

In this paper we introduce Structured Local Predictors (SLP) – A new formulation that considers the image labelling problem from a structured learning point of view. SLP are locally operating models, which provide a per-pixel labelling by exploiting contextual relations, learned from complex interactions between labels and a customizable intermediate representation of the image data. Our first key contribution is to handle flexible configurations of pairwise interactions between image pixels while allowing them to be made arbitrarily dependent on the image data. Moreover, we pose the parameter learning process as a convex, structured-learning problem, which can be efficiently solved

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Samuel Rota Bulò, Peter Kontschieder, Marcello Pelillo, Horst Bischof
• Keywords: Computer Vision, Cvpr 2012, Image Pixels, Intermediate Representation, Pairwise Interactions