A4 Conference proceedings

Comparison of Co-segmentation Methods for Wildlife Photo-identification

Publication Details
Authors: Popova Anastasia, Eerola Tuomas, Kälviäinen Heikki
Publisher: Springer Verlag (Germany): Series
Publication year: 2018
Language: English
Related Journal or Series Information: Lecture Notes in Computer Science
Title of parent publication: Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Poitiers, France, September 24–27, 2018, Proceedings
Journal acronym: LNCS
Volume number: 11182
Start page: 139
End page: 149
Number of pages: 11
ISBN: 978-3-030-01448-3
eISBN: 978-3-030-01449-0
ISSN: 0302-9743
eISSN: 1611-3349
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


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.

Last updated on 2019-13-03 at 12:00