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2018 | OriginalPaper | Chapter

Comparison of Co-segmentation Methods for Wildlife Photo-identification

Authors : Anastasia Popova, Tuomas Eerola, Heikki Kälviäinen

Published in: Advanced Concepts for Intelligent Vision Systems

Publisher: Springer International Publishing

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Abstract

Wildlife photo-identification is a commonly used technique to track animal populations over time. Nowadays, due to large image data sets, automated photo-identification is an emerging research topic. To improve the accuracy of identification methods, it is useful to segment the animal from the background. In this paper we evaluate the suitability of co-segmentation methods for this purpose. The basic idea in co-segmentation is to detect and to segment the common object in a set of images despite the different appearance of the object and different backgrounds. Such methods provide a promising approach to process large photo-identification databases for which manual or even semi-manual approaches are very time-consuming by making it unnecessary to annotate images to train supervised segmentation methods. We compare existing co-segmentation methods on challenging wildlife photo-identification images and show that the best methods obtain promising results on the task.

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Metadata
Title
Comparison of Co-segmentation Methods for Wildlife Photo-identification
Authors
Anastasia Popova
Tuomas Eerola
Heikki Kälviäinen
Copyright Year
2018
Book
Advanced Concepts for Intelligent Vision Systems

Print ISBN: 978-3-030-01448-3

Electronic ISBN: 978-3-030-01449-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01449-0_12

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