Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning and Inferring Image Segmentations using the GBP Typical Cut Algorithm
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Incremental learning of object detectors using a visual shape alphabet
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic spatial context models for scene content understanding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Single-Histogram class models for image segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Accurate semantic image labeling by fast geodesic propagation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
On parameter learning in CRF-based approaches to object class image segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Object class segmentation using reliable regions
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Inference scene labeling by incorporating object detection with explicit shape model
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Implicit scene context for object segmentation and classification
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Regionwise classification of building facade images
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Context models and out-of-context objects
Pattern Recognition Letters
Semantic parsing of street scenes from video
International Journal of Robotics Research
Retrieval of multiple instances of objects in videos
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Segmentation-based multi-class semantic object detection
Multimedia Tools and Applications
Orientation-aware scene understanding for mobile cameras
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Proceedings of the 20th ACM international conference on Multimedia
Learning domain knowledge for façade labelling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Beyond the line of sight: labeling the underlying surfaces
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Co-inference for multi-modal scene analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Segmentation and classification of objects with implicit scene context
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Really quick shift: image segmentation on a GPU
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Toward parts-based scene understanding with pixel-support parts-sparse pictorial structures
Pattern Recognition Letters
Using models of objects with deformable parts for joint categorization and segmentation of objects
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Computers and Electronics in Agriculture
Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition
International Journal of Computer Vision
Efficient semantic image segmentation with multi-class ranking prior
Computer Vision and Image Understanding
Image annotation by modeling Supporting Region Graph
Applied Intelligence
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Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying "tree" pixels indicates that pixels above and to the sides are more likely to be "sky" whereas pixels below are more likely to be "grass." Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed.In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multi-class image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.