Making large-scale support vector machine learning practical
Advances in kernel methods
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual Priming for Object Detection
International Journal of Computer Vision
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
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Multi-Aspect Detection of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Streetscenes: towards scene understanding in still images
Streetscenes: towards scene understanding in still images
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Sliding-Windows for Rapid Object Class Localization: A Parallel Technique
Proceedings of the 30th DAGM symposium on Pattern Recognition
A Performance Evaluation of Single and Multi-feature People Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
Figure/Ground assignment in natural images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context modeling in computer vision: techniques, implications, and applications
Multimedia Tools and Applications
Contextually guided semantic labeling and search for three-dimensional point clouds
International Journal of Robotics Research
Fusing LIDAR, camera and semantic information: A context-based approach for pedestrian detection
International Journal of Robotics Research
Multi-spectral dataset and its application in saliency detection
Computer Vision and Image Understanding
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Visual context provides cues about an object's presence, position and size within the observed scene, which should be used to increase the performance of object detection techniques. However, in computer vision, object detectors typically ignore this information. We therefore present a framework for visual-context-aware object detection. Methods for extracting visual contextual information from still images are proposed, which are then used to calculate a prior for object detection. The concept is based on a sparse coding of contextual features, which are based on geometry and texture. In addition, bottom-up saliency and object co-occurrences are exploited, to define auxiliary visual context. To integrate the individual contextual cues with a local appearance-based object detector, a fully probabilistic framework is established. In contrast to other methods, our integration is based on modeling the underlying conditional probabilities between the different cues, which is done via kernel density estimation. This integration is a crucial part of the framework which is demonstrated within the detailed evaluation. Our method is evaluated using a novel demanding image data set and compared to a state-of-the-art method for context-aware object detection. An in-depth analysis is given discussing the contributions of the individual contextual cues and the limitations of visual context for object detection.