Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
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
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Spatial and Feature Space Clustering: Applications in Image Analysis
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surfaces with occlusions from layered stereo
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Correspondence as energy-based segmentation
Image and Vision Computing
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-Based Modeling by Joint Segmentation
International Journal of Computer Vision
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating non-motion cues into 3D motion segmentation
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Change detection in rainfall and temperature patterns over India
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Lighting-Aware Segmentation of Microscopy Images for In Vitro Fertilization
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Expansion segmentation for visual collision detection and estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
Textural image segmentation using discrete cosine transform
CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
Improving motion-based object detection by incorporating object-specific knowledge
International Journal of Intelligent Information and Database Systems
Bayesian image segmentation with mean shift
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An unsupervised image segmentation algorithm based on the machine learning of appropriate features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Contextual Part Analogies in 3D Objects
International Journal of Computer Vision
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Image segmentation by MAP-ML estimations
IEEE Transactions on Image Processing
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
Logistic Stick-Breaking Process
The Journal of Machine Learning Research
Interactive segmentation with super-labels
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Supervised texture detection in images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
Incorporating non-motion cues into 3d motion segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Steerable semi-automatic segmentation of textured images
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Bayesian image segmentation using gaussian field priors
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Extraction of layers of similar motion through combinatorial techniques
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Energy-Based Geometric Multi-model Fitting
International Journal of Computer Vision
From ramp discontinuities to segmentation tree
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Automatic image segmentation by positioning a seed
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
An object recognition method using the improved snake algorithm
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Segmentation of liver tumor using efficient global optimal tree metrics graph cuts
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
Interactive Coherence-Based Façade Modeling
Computer Graphics Forum
Part analogies in sets of objects
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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Feature space clustering is a popular approach to image segmentation, in which a feature vector of local properties (such as intensity, texture or motion) is computed at each pixel. The feature space is then clustered, and each pixel is labeled with the cluster that contains its feature vector. A major limitation of this approach is that feature space clusters generally lack spatial coherence (i.e., they do not correspond to a compact grouping of pixels). In this paper, we propose a segmentation algorithm that operates simultaneously in feature space and in image space. We define an energy function over both a set of clusters and a labeling of pixels with clusters. In our framework, a pixel is labeled with a single cluster (rather than, for example, a distribution over clusters). Our energy function penalizes clusters that are a poor fit to the data in feature space, and also penalizes clusters whose pixels lack spatial coherence. The energy function can be efficiently minimized using graph cuts. Our algorithm can incorporate both parametric and non-parametric clustering methods. It can be applied to many optimization-based clustering methods, including k-means and k-medians, and can handle models which are very close in feature space. Preliminary results are presented on segmenting real and synthetic images, using both parametric and non-parametric clustering.