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Multiple Classifier Boosting and Tree-Structured Classifiers

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Machine Learning for Computer Vision

Part of the book series: Studies in Computational Intelligence ((SCI,volume 411))

Abstract

Visual recognition problems often involve classification of myriads of pixels, across scales, to locate objects of interest in an image or to segment images according to object classes. The requirement for high speed and accuracy makes the problems very challenging and has motivated studies on efficient classification algorithms. A novel multi-classifier boosting algorithm is proposed to tackle the multimodal problems by simultaneously clustering samples and boosting classifiers in Section 2. The method is extended into an online version for object tracking in Section 3. Section 4 presents a tree-structured classifier, called Super tree, to further speed up the classification time of a standard boosting classifier. The proposed methods are demonstrated for object detection, tracking and segmentation tasks.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Imperial College, London, UK

    Tae-Kyun Kim

  2. Department of Engineering, University of Cambridge, Cambridge, UK

    Roberto Cipolla

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  1. Tae-Kyun Kim
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  2. Roberto Cipolla
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Correspondence to Tae-Kyun Kim .

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Editors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom

    Roberto Cipolla

  2. Dipartimento di Matematica e Informatica, Università di Catania, Viale Andrea Doria 6, Catania, 95125, Catania, Italy

    Sebastiano Battiato

  3. Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, Catania, 95125, Italy

    Giovanni Maria Farinella

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Kim, TK., Cipolla, R. (2013). Multiple Classifier Boosting and Tree-Structured Classifiers. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Machine Learning for Computer Vision. Studies in Computational Intelligence, vol 411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28661-2_7

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  • DOI: https://doi.org/10.1007/978-3-642-28661-2_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28660-5

  • Online ISBN: 978-3-642-28661-2

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