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Semantic Texton Forests

  • Chapter
Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 285))

Abstract

The semantic texton forest is an efficient and powerful low-level feature which can be effectively employed in the semantic segmentation of images. As ensembles of decision trees that act directly on image pixels, semantic texton forests do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors. The nodes in the trees provide (i) an implicit hierarchical clustering into semantic textons, and (ii) an explicit local classification estimate. The bag of semantic textons combines a histogram of semantic textons over an image region with a region prior category distribution. The bag of semantic textons can be used by an SVM classifier to infer an image-level prior over categories, allowing the segmentation to emphasize those categories that the SVM believes to be present. We will examine the segmentation performance of semantic texton forests on two datasets including the VOC 2007 segmentation challenge.

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Author information

Authors and Affiliations

  1. Nokia, San Francisco, USA

    Matthew Johnson

  2. Microsoft Research, Cambridge, UK

    Jamie Shotton

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  1. Matthew Johnson
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  2. Jamie Shotton
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Editors and Affiliations

  1. Department of Engineering, University of Cambridge, CB2 1PZ, Cambridge, UK

    Roberto Cipolla

  2. Dipartimento di Matematica ed Informatica, University of Catania, Viale A. Doria 6, I, 95125, Catania, Italy

    Sebastiano Battiato  & Giovanni Maria Farinella  & 

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Johnson, M., Shotton, J. (2010). Semantic Texton Forests. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds) Computer Vision. Studies in Computational Intelligence, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12848-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-12848-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12847-9

  • Online ISBN: 978-3-642-12848-6

  • eBook Packages: EngineeringEngineering (R0)

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