Contextual Priming for Object Detection
International Journal of Computer Vision
Finding Pictures of Objects in Large Collections of Images
ECCV '96 Proceedings of the International Workshop on Object Representation in Computer Vision II
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Histograms of Oriented Gradients for Human Detection
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
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
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
Discriminative learning with latent variables for cluttered indoor scene understanding
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Discriminative learning with latent variables for cluttered indoor scene understanding
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Non-local characterization of scenery images: statistics, 3D reasoning, and a generative model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Context modeling in computer vision: techniques, implications, and applications
Multimedia Tools and Applications
A unified context assessing model for object categorization
Computer Vision and Image Understanding
Multi-scale stacked sequential learning
Pattern Recognition
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection
International Journal of Computer Vision
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Implicit scene context for object segmentation and classification
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Multi-class object layout with unsupervised image classification and object localization
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Context models and out-of-context objects
Pattern Recognition Letters
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Pedestrian recognition using second-order HOG feature
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
International Journal of Computer Vision
Cascaded classification of high resolution remote sensing images using multiple contexts
Information Sciences: an International Journal
Abnormal object detection by canonical scene-based contextual model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Contextual object detection using set-based classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Exploiting publicly available cartographic resources for aerial image analysis
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Ortho-image analysis for producing lane-level highway maps
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
International Journal of Computer Vision
Discriminative learning with latent variables for cluttered indoor scene understanding
Communications of the ACM
Dynamic objectness for adaptive tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Contextually guided semantic labeling and search for three-dimensional point clouds
International Journal of Robotics Research
Inference Methods for CRFs with Co-occurrence Statistics
International Journal of Computer Vision
Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates
International Journal of Computer Vision
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The sliding window approach of detecting rigid objects (such as cars) is predicated on the belief that the object can be identified from the appearance in a small region around the object. Other types of objects of amorphous spatial extent (e.g., trees, sky), however, are more naturally classified based on texture or color. In this paper, we seek to combine recognition of these two types of objects into a system that leverages "context" toward improving detection. In particular, we cluster image regions based on their ability to serve as context for the detection of objects. Rather than providing an explicit training set with region labels, our method automatically groups regions based on both their appearance and their relationships to the detections in the image. We show that our things and stuff (TAS) context model produces meaningful clusters that are readily interpretable, and helps improve our detection ability over state-of-the-art detectors. We also present a method for learning the active set of relationships for a particular dataset. We present results on object detection in images from the PASCAL VOC 2005/2006 datasets and on the task of overhead car detection in satellite images, demonstrating significant improvements over state-of-the-art detectors.