Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
The nature of statistical learning theory
The nature of statistical learning theory
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Advances in Component Based Face Detection
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning a restricted Bayesian network for object detection
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
Dissimilarity between two skeletal trees in a context
Pattern Recognition
Image retrieval using query by contextual example
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Context Driven Focus of Attention for Object Detection
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Integrating Visual Context and Object Detection within a Probabilistic Framework
Attention in Cognitive Systems
ConVeS: a context verification framework for object recognition system
Proceedings of the 2009 conference on Information Science, Technology and Applications
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
A framework for visual-context-aware object detection in still images
Computer Vision and Image Understanding
Context based object categorization: A critical survey
Computer Vision and Image Understanding
Context modeling in computer vision: techniques, implications, and applications
Multimedia Tools and Applications
Geotagging in multimedia and computer vision--a survey
Multimedia Tools and Applications
A unified context assessing model for object categorization
Computer Vision and Image Understanding
Improving image categorization by using multiple instance learning with spatial relation
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
The Visual Extent of an Object
International Journal of Computer Vision
Context-Aware Semi-Local Feature Detector
ACM Transactions on Intelligent Systems and Technology (TIST)
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
International Journal of Computer Vision
Probabilistic semantic component descriptor
Multimedia Tools and Applications
Cascaded classification of high resolution remote sensing images using multiple contexts
Information Sciences: an International Journal
Hierarchical discriminative framework for detecting tubular structures in 3D images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Constraining image object search by multi-scale spectral residue analysis
Pattern Recognition Letters
Branch&Rank for Efficient Object Detection
International Journal of Computer Vision
Image annotation by modeling Supporting Region Graph
Applied Intelligence
Hi-index | 0.00 |
In this study, a discriminative detector for object context is designed and tested. The context-feature is simple to implement, feed-forward, and effective across multiple object types in a street-scenes environment.Using context alone, we demonstrate robust detection of locations likely to contain bicycles, cars, and pedestrians. Furthermore, experiments are conducted so as to address several open questions regarding visual context. Specifically, it is demonstrated that context may be determined from low level visual features (simple color and texture descriptors) sampled over a wide receptive field. At least for the framework tested, high level semantic knowledge, e.g, the nature of the surrounding objects, is superfluous. Finally, it is shown that when the target object is unambiguously visible, context is only marginally useful.