Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The generalized patchmatch correspondence algorithm
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Nonparametric Scene Parsing via Label Transfer
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Optimizing color consistency in photo collections
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Label transfer exploiting three-dimensional structure for semantic segmentation
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
DARWIN: a framework for machine learning and computer vision research and development
The Journal of Machine Learning Research
PatchNet: a patch-based image representation for interactive library-driven image editing
ACM Transactions on Graphics (TOG)
Data-driven interactive 3D medical image segmentation based on structured patch model
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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We address the problem of semantic segmentation, or multi-class pixel labeling, by constructing a graph of dense overlapping patch correspondences across large image sets. We then transfer annotations from labeled images to unlabeled images using the established patch correspondences. Unlike previous approaches to non-parametric label transfer our approach does not require an initial image retrieval step. Moreover, we operate on a graph for computing mappings between images, which avoids the need for exhaustive pairwise comparisons. Consequently, we can leverage offline computation to enhance performance at test time. We conduct extensive experiments to analyze different variants of our graph construction algorithm and evaluate multi-class pixel labeling performance on several challenging datasets.