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
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.
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