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Scalable real-time object recognition and segmentation via cascaded, discriminative Markov random fields

Scalable real-time object recognition and segmentation via cascaded, discriminative Markov random fields,10.1109/ROBOT.2010.5509209,Paul Vernaza,Danie

Scalable real-time object recognition and segmentation via cascaded, discriminative Markov random fields  
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We present a method for real-time simultaneous object recognition and segmentation based on cascaded discrim- inative Markov random fields. A Markov random field models coupling between the labels of adjacent image regions. The MRF affinities are learned as linear functions of image features in a structured max-margin framework that admits a solution via convex optimization. In contrast to other known MRF/CRF- based approaches, our method classifies in real-time and has computational complexity that scales only logarithmically in the number of object classes. We accomplish this by applying a cascade of binary MRF-classifiers in a way similar to error-correcting output coding for general multiclass learning problems. Inference in this model is exact and can be performed very efficiently using graph cuts. Experimental results are shown that demonstrate a marked improvement in classification accuracy over purely local methods.
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