Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Short note: O(N) implementation of the fast marching algorithm
Journal of Computational Physics
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
International Journal of Computer Vision
Image-based street-side city modeling
ACM SIGGRAPH Asia 2009 papers
Accurate semantic image labeling by fast geodesic propagation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Supervised label transfer for semantic segmentation of street scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Partial similarity based nonparametric scene parsing in certain environment
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Projective analysis for 3D shape segmentation
ACM Transactions on Graphics (TOG)
Deformable model for estimating clothed and naked human shapes from a single image
The Visual Computer: International Journal of Computer Graphics
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In this paper we propose a novel semantic label transfer method using supervised geodesic propagation (SGP). We use supervised learning to guide the seed selection and the label propagation. Given an input image, we first retrieve its similar image set from annotated databases. A Joint Boost model is learned on the similar image set of the input image. Then the recognition proposal map of the input image is inferred by this learned model. The initial distance map is defined by the proposal map: the higher probability, the smaller distance. In each iteration step of the geodesic propagation, the seed is selected as the one with the smallest distance from the undetermined superpixels. We learn a classifier as an indicator to indicate whether to propagate labels between two neighboring superpixels. The training samples of the indicator are annotated neighboring pairs from the similar image set. The geodesic distances of its neighbors are updated according to the combination of the texture and boundary features and the indication value. Experiments on three datasets show that our method outperforms the traditional learning based methods and the previous label transfer method for the semantic segmentation work.