Contextual Priming for Object Detection
International Journal of Computer Vision
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Context and Hierarchy in a Probabilistic Image Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Learning Latent Tree Graphical Models
The Journal of Machine Learning Research
A Tree-Based Context Model for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trees and beyond: exploiting and improving tree-structured graphical models
Trees and beyond: exploiting and improving tree-structured graphical models
Abnormal object detection by canonical scene-based contextual model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Contextual word spotting in historical manuscripts using Markov logic networks
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
A boosting approach for the simultaneous detection and segmentation of generic objects
Pattern Recognition Letters
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The context of an image encapsulates rich information about how natural scenes and objects are related to each other. Such contextual information has the potential to enable a coherent understanding of natural scenes and images. However, context models have been evaluated mostly based on the improvement of object recognition performance even though it is only one of many ways to exploit contextual information. In this paper, we present a new scene understanding problem for evaluating and applying context models. We are interested in finding scenes and objects that are ''out-of-context''. Detecting ''out-of-context'' objects and scenes is challenging because context violations can be detected only if the relationships between objects are carefully and precisely modeled. To address this problem, we evaluate different sources of context information, and present a graphical model that combines these sources. We show that physical support relationships between objects can provide useful contextual information for both object recognition and out-of-context detection.