Foundations and Trends® in Computer Graphics and Vision
Grass, scrub, trees and random forest
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Local label descriptor for example based semantic image labeling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Joint classification-regression forests for spatially structured multi-object segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
A parts-based multi-scale method for symbol recognition
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Non-rigid target tracking based on 'flow-cut' in pair-wise frames with online hough forests
Proceedings of the 21st ACM international conference on Multimedia
| Hi-index | 0.00 |
In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of object classes in a given image. Different object class labels will not be randomly distributed over an image but usually form coherently labelled regions. In this work we provide a way to incorporate this topological information in the popular random forest framework for performing low-level, unary classification. Our paper has several contributions: First, we show how random forests can be augmented with structured label information. In the second part, we introduce a novel data splitting function that exploits the joint distributions observed in the structured label space for learning typical label transitions between object classes. Finally, we provide two possibilities for integrating the structured output predictions into concise, semantic labellings. In our experiments on the challenging MSRC and CamVid databases, we compare our method to standard random forest and conditional random field classification results.