OOPLSA '86 Conference proceedings on Object-oriented programming systems, languages and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Using Dependent Regions for Object Categorization in a Generative Framework
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Image-based procedural modeling of facades
ACM SIGGRAPH 2007 papers
Learning a Knowledge Base of Ontological Concepts for High-Level Scene Interpretation
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Efficient Learning of Relational Object Class Models
International Journal of Computer Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Deriving distance metrics from generality relations
Pattern Recognition Letters
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
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Augmenting vision systems with high-level knowledge and reasoning can improve lower-level vision processes, by using richer and more structured information. In this paper we tackle the problem of delimiting conceptual elements of street views based on spatial relations between lower-level components, e.g. the element 'house' is composed of windows and a door in a spatial arrangement. We use structured data: each concept can be seen as a graph representing spatial relations between components, e.g. in terms of right, up, close. We employ a distance-based approach between logical interpretations to match parts of images with known examples and provide an experimental evaluation on real images.