Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
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
Machine Learning
Towards Multi-View Object Class Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
On scene interpretation with description logics
Image and Vision Computing
The Tower of Knowledge Scheme for Learning in Computer Vision
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Bottom-Up/Top-Down Image Parsing with Attribute Grammar
IEEE Transactions on Pattern Analysis and Machine Intelligence
A semantics-based decision theory region analyzer
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Learning in computer vision: some thoughts
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Plane-based object categorisation using relational learning
Machine Learning
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We propose a novel logic-rule learning approach for the Tower of Knowledge (ToK) architecture, based on Markov logic networks, for scene interpretation This approach is in the spirit of the recently proposed Markov logic networks of machine learning Its purpose is to learn the soft-constraint logic rules for labelling the components of a scene This approach also benefits from the architecture of ToK, in reasoning whether a component in a scene has the right characteristics in order to fulfil the functions a label implies, from the logic point of view One significant advantage of the proposed approach, rather than the previous versions of ToK, is its automatic logic learning capability such that the manual insertion of logic rules is not necessary Experiments of building scene interpretation illustrate the promise of this approach.