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Authors: Sarah Bertrand 1 ; Guillaume Cerutti 2 and Laure Tougne 1

Affiliations: 1 Univ. Lyon, France ; 2 INRIA, France

Keyword(s): Bark, Leaf, Tree Recognition, Smart-phone.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis

Abstract: In this paper, we propose a botanical approach for tree species classification through automatic bark analysis. The proposed method is based on specific descriptors inspired by the characterization keys used by botanists, from visual bark texture criteria. The descriptors and the recognition system are developed in order to run on a mobile device, without any network access. Our obtained results show a similar rate when compared to the state of the art in tree species identification from bark images with a small feature vector. Furthermore, we also demonstrate that the consideration of the bark identification significantly improves the performance of tree classification based on leaf only.

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Paper citation in several formats:
Bertrand, S.; Cerutti, G. and Tougne, L. (2017). Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP; ISBN 978-989-758-225-7; ISSN 2184-4321, SciTePress, pages 435-442. DOI: 10.5220/0006108504350442

@conference{visapp17,
author={Sarah Bertrand. and Guillaume Cerutti. and Laure Tougne.},
title={Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP},
year={2017},
pages={435-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006108504350442},
isbn={978-989-758-225-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP
TI - Bark Recognition to Improve Leaf-based Classification in Didactic Tree Species Identification
SN - 978-989-758-225-7
IS - 2184-4321
AU - Bertrand, S.
AU - Cerutti, G.
AU - Tougne, L.
PY - 2017
SP - 435
EP - 442
DO - 10.5220/0006108504350442
PB - SciTePress

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