We are analyzing https://link.springer.com/article/10.1007/s11263-007-0109-1.

Title:
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context | International Journal of Computer Vision
Description:
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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Keywords {🔍}

vision, computer, conference, proceedings, recognition, vol, google, scholar, article, pattern, ieee, object, segmentation, image, international, learning, june, machine, springer, european, journal, texture, shotton, winn, random, analysis, rother, model, transactions, york, layout, criminisi, semantic, features, access, shape, intelligence, eds, october, privacy, cookies, content, data, information, research, search, understanding, context, visual, class,

Topics {✒️}

conditional random field piecewise training methods month download article/chapter carsten rother & antonio criminisi conditional random fields approximate nearest-neighbour search multi-class object recognition unsupervised scale-invariant learning piecewise training grabcut—interactive foreground extraction real-time keypoint recognition semantic photo synthesis interleaved object categorization generative-model based vision discriminative random fields related subjects context information efficiently interactive image segmentation automatic visual understanding accurate image segmentation understanding belief propagation visual object recognition object class recognition specific object instances privacy choices/manage cookies image understanding semantic segmentation random feature selection check access fixed image vocabulary instant access full article pdf article international journal scale-invariant keypoints jointly modeling texture distinctive image features multiview object detection rapid object detection unsupervised segmentation approximate bayesian inference linear spatial filters bilayer video segmentation exemplar-based inpainting web-based tool image segmentation learning object classes 7-class corel subset high-dimensional spaces jointly model patterns shared boosting

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