Texture Segmentation by Contractive Decomposition and Planar Grouping
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Human-centered picture slideshow personalization for mobile devices
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
The generalized patchmatch correspondence algorithm
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Segmentation via ncuts and lossy minimum description length: a unified approach
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Interactive video layer decomposition and matting
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Scene understanding through autonomous interactive perception
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
A segmentation quality measure based on rich descriptors and classification methods
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs
Computer Vision and Image Understanding
SymCity: feature selection by symmetry for large scale image retrieval
Proceedings of the 20th ACM international conference on Multimedia
PatchNet: a patch-based image representation for interactive library-driven image editing
ACM Transactions on Graphics (TOG)
Detecting, segmenting and tracking unknown objects using multi-label MRF inference
Computer Vision and Image Understanding
Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition
International Journal of Computer Vision
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There is a huge diversity of definitions of "visually meaningful" image segments, ranging from simple uniformly colored segments, textured segments, through symmetric patterns, and up to complex semantically meaningful objects. This diversity has led to a wide range of different approaches for image segmentation. In this paper we present a single unified framework for addressing this problem --- "Segmentation by Composition". We define a good image segment as one which can be easily composed using its own pieces, but is difficult to compose using pieces from other parts of the image. This non-parametric approach captures a large diversity of segment types, yet requires no pre-definition or modelling of segment types, nor prior training. Based on this definition, we develop a segment extraction algorithm --- i.e., given a single point-of-interest, provide the "best" image segment containing that point. This induces a figure-ground image segmentation, which applies to a range of different segmentation tasks: single image segmentation, simultaneous co-segmentation of several images, and class-based segmentations.