Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Streetscenes: towards scene understanding in still images
Streetscenes: towards scene understanding in still images
ACM SIGGRAPH Asia 2008 papers
ACM SIGGRAPH Asia 2008 papers
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic object classes in video: A high-definition ground truth database
Pattern Recognition Letters
Segmentation and Recognition Using Structure from Motion Point Clouds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Computer Vision
Image-based street-side city modeling
ACM SIGGRAPH Asia 2009 papers
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Supervised geodesic propagation for semantic label transfer
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Visual dictionary learning for joint object categorization and segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Beyond the line of sight: labeling the underlying surfaces
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Label transfer exploiting three-dimensional structure for semantic segmentation
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
Detecting changes in images of street scenes
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Projective analysis for 3D shape segmentation
ACM Transactions on Graphics (TOG)
Hi-index | 0.00 |
In this paper, we propose a robust supervised label transfer method for the semantic segmentation of street scenes. Given an input image of street scene, we first find multiple image sets from the training database consisting of images with annotation, each of which can cover all semantic categories in the input image. Then, we establish dense correspondence between the input image and each found image sets with a proposed KNN-MRF matching scheme. It is followed by a matching correspondences classification that tries to reduce the number of semantically incorrect correspondences with trained matching correspondences classification models for different categories. With those matching correspondences classified as semantically correct correspondences, we infer the confidence values of each super pixel belonging to different semantic categories, and integrate them and spatial smoothness constraint in a markov random field to segment the input image. Experiments on three datasets show our method outperforms the traditional learning based methods and the previous nonparametric label transfer method, for the semantic segmentation of street scenes.