Abstract
Wildlife photo-identification is a commonly used technique to identify and track individuals of wild animal populations over time. It has various applications in behavior and population demography studies. Nowadays, mostly due to large and labor-intensive image data sets, automated photo-identification is an emerging research topic. In this paper, the first steps towards automatic individual identification of the critically endangered Saimaa ringed seal (Phoca hispida saimensis) are taken. Ringed seals have a distinctive permanent pelage pattern that is unique to each individual making the image-based identification possible. We propose a superpixel classification based method for the segmentation of ringed seal in images to eliminate the background and to simplify the identification. The proposed segmentation method is shown to achieve a high segmentation accuracy with challenging image data. Furthermore, we show that using the obtained segmented images promising identification results can be obtained even with a simple texture feature based approach. The proposed method uses general texture classification techniques and can be applied also to other animal species with a unique fur or skin pattern.
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The authors would like to thank the Wildlife Photo-ID Network funded by the Finnish Cultural Foundation.
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Zhelezniakov, A. et al. (2015). Segmentation of Saimaa Ringed Seals for Identification Purposes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_21
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DOI: https://doi.org/10.1007/978-3-319-27863-6_21
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