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Shotton et al., 2006 - Google Patents

Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation

Shotton et al., 2006

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Document ID
10493345110922196547
Author
Shotton J
Winn J
Rother C
Criminisi A
Publication year
Publication venue
Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9

External Links

Snippet

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 …
Continue reading at www.microsoft.com (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
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    • G06K9/6201—Matching; Proximity measures
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    • G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
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    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4652—Extraction of features or characteristics of the image related to colour
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    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
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