21. J. Shotton, J. Winn, C. Rother, A. Criminisi: TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation
This poster 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 viewpoints, ii) the 7-class
Corel subset and iii) the 7-class Sowerby database. The proposed
algorithm gives competitive results both for highly textured ( e.g.
grass, trees), highly structured (e.g. cars, faces, bikes, aeroplanes)
and articulated objects (e.g. body, cow).