TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation

Abstract. 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 photographs of real objects viewed under general lighting conditions, poses and vie...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi
• Keywords: Accurate Image Segmentation, Computer Vision, Discriminative Model Exploits, ECCV 2006, Random Feature Selection, Segmentation Accuracy, Semantic Segmentation