# Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

NIPS, (2012): 109-117

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Abstract

Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, we consider f...More

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Introduction

- Multi-class image segmentation and labeling is one of the most challenging and actively studied problems in computer vision.
- The accuracy of these approaches is necessarily restricted by the accuracy of unsupervised image segmentation, which is used to compute the regions on which the model operates.
- This limits the ability of region-based approaches to produce accurate label assignments around complex object boundaries, significant progress has been made [9, 13, 14]

Highlights

- Multi-class image segmentation and labeling is one of the most challenging and actively studied problems in computer vision
- Basic conditional random field (CRF) models are composed of unary potentials on individual pixels or image patches and pairwise potentials on neighboring pixels or patches [19, 23, 7, 5]
- In order to improve segmentation and labeling accuracy, researchers have expanded the basic CRF framework to incorporate hierarchical connectivity and higher-order potentials defined on image regions [8, 12, 9, 13]
- We evaluate the presented algorithm on two standard benchmarks for multi-class image segmentation and labeling
- The first is the MSRC-21 dataset, which consists of 591 color images of size 320 × 213 with corresponding ground truth labelings of 21 object classes [19]
- We have presented a highly efficient approximate inference algorithm for fully connected CRF models

Results

- The authors evaluate the presented algorithm on two standard benchmarks for multi-class image segmentation and labeling.
- The first is the MSRC-21 dataset, which consists of 591 color images of size 320 × 213 with corresponding ground truth labelings of 21 object classes [19].
- The second is the PASCAL VOC 2010 dataset, which contains 1928 color images of size approximately 500 × 400, with a total of 20 object classes and one background class [3].
- The inference algorithm was implemented in a single CPU thread

Conclusion

- The authors have presented a highly efficient approximate inference algorithm for fully connected CRF models.
- The authors' results demonstrate that dense pixel-level connectivity leads to significantly more accurate pixel-level classification performance.
- The authors' single-threaded implementation processes benchmark images in a fraction of a second and the algorithm can be parallelized for further performance gains

Funding

- Philipp Krahenbuhl was supported in part by a Stanford Graduate Fellowship. background bird road cat void

Reference

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