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Associative Hierarchical CRFs for Object Class Image Segmentation

Associative Hierarchical CRFs for Object Class Image Segmentation. Ľubor Ladický 1 , Chris Russell 1 , Pushmeet Kohli 2 , Philip H.S. Torr 1. 1 Oxford Brookes University. 2 Microsoft Research, Cambridge. Our contribution. Problem. Results.

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Associative Hierarchical CRFs for Object Class Image Segmentation

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  1. Associative Hierarchical CRFs for Object Class Image Segmentation Ľubor Ladický1, Chris Russell1, Pushmeet Kohli2, Philip H.S. Torr1 1 Oxford Brookes University 2 Microsoft Research, Cambridge Our contribution Problem Results • To label each pixel of an image with its object class • Robust PN potentials reformulated using pairwise graph with auxiliary variables taking label from the label-set or label free • This formulation generalized to allow unary and pairwise potentials at segment level and any large hierarchy of quantization-levels • Resulting model still solvable using graphcut-based algorithms • Limited to PN-like associative interlayer connections Standard Approaches • Problem formulated as a pairwise CRF at certain quantization level (pixels, super-pixels, ..) • Pixel CRF • Lacks long range interactions • Local features less informative • Results oversmoothed • Segment CRF • Informative features based on large regions of the image • Enforces consistency at a single larger scale • Fixes the quantisation of the image and either over or under segment objects. • Provides no way to recover from a misleading segmentation • Courser segmentation provides more information but boundaries may not be label consistent • Pixel CRF with Potentials enforcing segment consistency • Potentials take the form of Robust PN model (Kohli, Ladický, Torr – CVPR09) • Enforces consistency in segments as weak constraints • Allows incorporations of multiple segmentations • Limited to unary and pairwise potentials over pixels • Informative features based on many differently sized regions • Enforces consistency at all scales and between layers • Robust to misleading segmentation. MSRC dataset • Common models • as special cases • Energy formulation Training of the model • Pixel unary potentials trained from multiple features (Texton Colour, Location, HOG) using similar procedure to TextonBoost • Segment unary potentials trained by boosting histograms of same dense features • Pairwise potentials calculated as a function of difference of colour or EMD distance of colour histograms • To tune the weight associated with different layers, a greedy algorithm is used VOC2008 dataset

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