Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transferring color to greyscale images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Fragment-based image completion
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
Keyframe-based tracking for rotoscoping and animation
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH Asia 2008 papers
A multiple instance learning based framework for semantic image segmentation
Multimedia Tools and Applications
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Seed image selection in interactive cosegmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance
International Journal of Computer Vision
Interactive segmentation with super-labels
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
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Segmenting an image into semantically meaningful parts is a fundamental and challenging task in computer vision. Automatic methods are able to segment an image into coherent regions, but such regions generally do not correspond to complete meaningful parts. In this paper, we show that even a single training example can greatly facilitate the induction of a semantically meaningful segmentation on novel images within the same domain: images depicting the same, or similar, objects in a similar setting. Our approach constructs a non-parametric representation of the example segmentation by selecting patch-based representatives. This allows us to represent complex semantic regions containing a large variety of colors and textures. Given an input image, we first partition it into small homogeneous fragments, and the possible labelings of each fragment are assessed using a robust voting procedure. Graph-cuts optimization is then used to label each fragment in a globally optimal manner.