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
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
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
Context based object detection from video
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
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In this paper we introduce and exploit the concept of contextual rules in the field of object detection. These rules are defined as associations between different object likelihood maps and are learned from given examples. The contextual rules can be used to prime regions where a target object category occurs in an image given areas of other object categories. The principal idea is to locate several basic object categories in an image and then use this information to infer object likelihood maps for other object categories. The proposed framework itself is general and not limited to specific object categories. For demonstrating our approach, we use likely occurrences of pedestrians and windows in urban scenes, extracted by a technique employing visual context, and use them to prime for shop logos.