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

We present a method for real-time simultaneous object recognition and segmentation based on cascaded discriminative 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 stru...

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Bibliographic Details
Main Authors: Vernaza, Paul, Lee, Daniel D
Format: Conference Proceeding
Language:English
Subjects:
Binary codes
Color
Computational complexity
Image segmentation
Laboratories
Markov random fields
Object recognition
Robotics and automation
Robots
USA Councils
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Summary:We present a method for real-time simultaneous object recognition and segmentation based on cascaded discriminative 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.
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2010.5509209