Contextual Priming for Object Detection
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Integration of visual and shape attributes for object action complexes
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Context based object categorization: A critical survey
Computer Vision and Image Understanding
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
| Hi-index | 0.00 |
Robust category-level object recognition is currently a major goal for the Computer Vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual information in the form of object co-ocurrences and spatial contraints has been successfully applied to reduce the inherent uncertainty of the visual world. Recently, Choi et al. [5] propose the use of a tree-structured graphical model to capture contextual relations among objects. Under this model there is only one possible fixed contextual relation among subsets of objects. In this work we extent Choi et al. approach by using a mixture model to consider the case that contextual relations among objects depend on scene type. Our experiments highlight the advantages of our proposal, showing that the adaptive specialization of contextual relations improves object recognition and object detection performances.