Local Grayvalue Invariants for Image Retrieval
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A revolution: belief propagation in graphs with cycles
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Fast Approximate Energy Minimization via Graph Cuts
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Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
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Finding Deformable Shapes Using Loopy Belief Propagation
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Video Google: A Text Retrieval Approach to Object Matching in Videos
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Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
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Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
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Image Parsing: Unifying Segmentation, Detection, and Recognition
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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A Hierarchical Field Framework for Unified Context-Based Classification
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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International Journal of Computer Vision
Recovering Surface Layout from an Image
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Putting Objects in Perspective
International Journal of Computer Vision
Sparse Long-Range Random Field and Its Application to Image Denoising
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Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
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International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Graphical Models, Exponential Families, and Variational Inference
Graphical Models, Exponential Families, and Variational Inference
Multi-class image segmentation using conditional random fields and global classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions
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Global Stereo Reconstruction under Second-Order Smoothness Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning color names for real-world applications
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The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Evaluating Color Descriptors for Object and Scene Recognition
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Object Detection with Discriminatively Trained Part-Based Models
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Learning what and how of contextual models for scene labeling
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Graph cut based inference with co-occurrence statistics
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Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Describing Reflectances for Color Segmentation Robust to Shadows, Highlights, and Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An Efficient Approach to Semantic Segmentation
International Journal of Computer Vision
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Identity inference: generalizing person re-identification scenarios
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Efficient semantic image segmentation with multi-class ranking prior
Computer Vision and Image Understanding
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The Hierarchical Conditional Random Field (HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales. At higher scales in the image, this representation yields an oversimplified model since multiple classes can be reasonably expected to appear within large regions. This simplified model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combination of labels, penalizing only unlikely combinations of classes. We also propose an effective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.