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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can(More)
The Hierarchical Conditional Random Field (HCRF) model have been successfully applied to a number of image labeling problems, including image segmenta-tion. However, existing HCRF models of image segmenta-tion do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple(More)
— Object detection is an open research problem in computer vision, and most important recent advances make use of parts-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into the parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based(More)
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