A Recursive Approach For Multiclass Support Vector Machine - Application to Automatic Classification of Endomicroscopic Videos

Alexis Zubiolo, Grégoire Malandain, Barbara André, Éric Debreuve

2014

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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