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Authors: Alexis Zubiolo 1 ; Grégoire Malandain 1 ; Barbara André 2 and Éric Debreuve 1

Affiliations: 1 University of Nice-Sophia Antipolis/CNRS/Inria, France ; 2 Mauna Kea Technologies, France

ISBN: 978-989-758-003-1

Keyword(s): Multiclass classification, Supervised Learning, Hierarchical Approach, Graph Minimum-cut, Support Vector Machine (SVM).

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Early and Biologically-Inspired Vision ; Image and Video Analysis

Abstract: The two classical steps of image or video classification are: image signature extraction and assignment of a class based on this image signature. The class assignment rule can be learned from a training set composed of sample images manually classified by experts. This is known as supervised statistical learning. The well-known Support Vector Machine (SVM) learning method was designed for two classes. Among the proposed extensions to multiclass (three classes or more), the one-versus-one and one-versus-all approaches are the most popular ones. This work presents an alternative approach to extending the original SVM method to multiclass. A tree of SVMs is built using a recursive learning strategy, achieving a linear worst-case complexity in terms of number of classes for classification. During learning, at each node of the tree, a bi-partition of the current set of classes is determined to optimally separate the current classification problem into two sub-problems. Rather than relying on an exhaustive search among all possible subsets of classes, the partition is obtained by building a graph representing the current problem and looking for a minimum cut of it. The proposed method is applied to classification of endomicroscopic videos and compared to classical multiclass approaches. (More)

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Paper citation in several formats:
Zubiolo, A.; Malandain, G.; André, B. and Debreuve, É. (2014). A Recursive Approach For Multiclass Support Vector Machine - Application to Automatic Classification of Endomicroscopic Videos.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 441-447. DOI: 10.5220/0004654704410447

@conference{visapp14,
author={Alexis Zubiolo. and Grégoire Malandain. and Barbara André. and Éric Debreuve.},
title={A Recursive Approach For Multiclass Support Vector Machine - Application to Automatic Classification of Endomicroscopic Videos},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={441-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004654704410447},
isbn={978-989-758-003-1},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - A Recursive Approach For Multiclass Support Vector Machine - Application to Automatic Classification of Endomicroscopic Videos
SN - 978-989-758-003-1
AU - Zubiolo, A.
AU - Malandain, G.
AU - André, B.
AU - Debreuve, É.
PY - 2014
SP - 441
EP - 447
DO - 10.5220/0004654704410447

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