Authors
Jamie Shotton, John Winn, Carsten Rother, Antonio Criminisi
Publication date
2006
Conference
Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9
Pages
1-15
Publisher
Springer Berlin Heidelberg
Description
This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating these classifiers in a conditional random field. Efficient training of the model on very large datasets is achieved by exploiting both random feature selection and piecewise training methods.
High classification and segmentation accuracy are demonstrated on three different databases: i) our own 21-object class database of …
Total citations
Scholar articles
J Shotton, J Winn, C Rother, A Criminisi - Computer Vision–ECCV 2006: 9th European …, 2006