Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Database Systems: The Complete Book
Database Systems: The Complete Book
Bottom-Up/Top-Down Image Parsing with Attribute Grammar
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Logical and Relational Learning
Logical and Relational Learning
Recursive Segmentation and Recognition Templates for Image Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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While relational representations have been popular in early work on syntactic and structural pattern recognition, they are rarely used in contemporary approaches to computer vision due to their pure symbolic nature. The recent progress and successes in combining statistical learning principles with relational representations motivates us to reinvestigate the use of such representations. More specifically, we show that statistical relational learning can be successfully used for hierarchical image understanding. We employ kLog, a new logical and relational language for learning with kernels to detect objects at different levels in the hierarchy. The key advantage of kLog is that both appearance features and rich, contextual dependencies between parts in a scene can be integrated in a principled and interpretable way to obtain a qualitative representation of the problem. At each layer, qualitative spatial structures of parts in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and successfully detect corners, windows, doors, and individual houses.