TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits textur...

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Vydáno v:International journal of computer vision Ročník 81; číslo 1; s. 2 - 23
Hlavní autoři: Shotton, Jamie, Winn, John, Rother, Carsten, Criminisi, Antonio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.01.2009
Springer Nature B.V
Témata:
Artificial Intelligence
Computer Imaging
Computer Science
Computer vision
Feature selection
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Studies
Vision
Context
Textons
Conditional random field
Segmentation
Layout
Semantic image segmentation
Image understanding
Boosting
Piecewise training
Object recognition
Texture
ISSN:0920-5691, 1573-1405
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Shrnutí:This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters , novel features based on textons, which jointly model patterns of texture and their spatial layout. 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 the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing 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 used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695–702, June 2004 ), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.).
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-007-0109-1