Original Contribution: Stacked generalization
Neural Networks
Combining Belief Networks and Neural Networks for Scene Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Alternate Objective Function for Markovian Fields
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
The Journal of Machine Learning Research
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Search-based structured prediction
Machine Learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Training an active random field for real-time image denoising
IEEE Transactions on Image Processing
Image Segmentation with a Unified Graphical Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MRF Energy Minimization and Beyond via Dual Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting inference for approximate parameter learning in discriminative fields: an empirical study
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Improving predictions using aggregate information
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
International Journal of Computer Vision
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
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Relating things and stuff by high-order potential modeling
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
On the use of regions for semantic image segmentation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Enhancing robot perception using human teammates
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Using coarse information for real valued prediction
Data Mining and Knowledge Discovery
Multiscale Symmetric Part Detection and Grouping
International Journal of Computer Vision
Sparse reconstruction for weakly supervised semantic segmentation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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In this work we propose a hierarchical approach for labeling semantic objects and regions in scenes. Our approach is reminiscent of early vision literature in that we use a decomposition of the image in order to encode relational and spatial information. In contrast to much existing work on structured prediction for scene understanding, we bypass a global probabilistic model and instead directly train a hierarchical inference procedure inspired by the message passing mechanics of some approximate inference procedures in graphical models. This approach mitigates both the theoretical and empirical difficulties of learning probabilistic models when exact inference is intractable. In particular, we draw from recent work in machine learning and break the complex inference process into a hierarchical series of simple machine learning subproblems. Each subproblem in the hierarchy is designed to capture the image and contextual statistics in the scene. This hierarchy spans coarse-to-fine regions and explicitly models the mixtures of semantic labels that may be present due to imperfect segmentation. To avoid cascading of errors and overfitting, we train the learning problems in sequence to ensure robustness to likely errors earlier in the inference sequence and leverage the stacking approach developed by Cohen et al.