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Authors: Rémi Ratajczak 1 ; Sarah Bertrand 2 ; Carlos Crispim-Junior 2 and Laure Tougne 2

Affiliations: 1 Univ Lyon, Lyon 2, LIRIS, F-69676 Lyon, France, Unité Cancer et Environnement, Centre Léon Bérard, Lyon, France, Agence de l’Environnement et de la Maítrise de l’Energie, Angers and France ; 2 Univ Lyon, Lyon 2, LIRIS, F-69676 Lyon and France

ISBN: 978-989-758-354-4

Keyword(s): Bark Recognition, Texture Classification, Color Quantification, Dimensionality Reduction, Data Fusion.

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

Abstract: In this study, we propose to address the difficult task of bark recognition in the wild using computationally efficient and compact feature vectors. We introduce two novel generic methods to significantly reduce the dimensions of existing texture and color histograms with few losses in accuracy. Specifically, we propose a straightforward yet efficient way to compute Late Statistics from texture histograms and an approach to iteratively quantify the color space based on domain priors. We further combine the reduced histograms in a late fusion manner to benefit from both texture and color cues. Results outperform state-of-the-art methods by a large margin on four public datasets respectively composed of 6 bark classes (BarkTex, NewBarkTex), 11 bark classes (AFF) and 12 bark classes (Trunk12). In addition to these experiments, we propose a baseline study on Bark-101, a new challenging dataset including manually segmented images of 101 bark classes that we release publicly.

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Paper citation in several formats:
Ratajczak, R.; Bertrand, S.; Crispim-Junior, C. and Tougne, L. (2019). Efficient Bark Recognition in the Wild.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-354-4, pages 240-248. DOI: 10.5220/0007361902400248

@conference{visapp19,
author={Rémi Ratajczak. and Sarah Bertrand. and Carlos Crispim{-}Junior. and Laure Tougne.},
title={Efficient Bark Recognition in the Wild},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2019},
pages={240-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007361902400248},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Efficient Bark Recognition in the Wild
SN - 978-989-758-354-4
AU - Ratajczak, R.
AU - Bertrand, S.
AU - Crispim-Junior, C.
AU - Tougne, L.
PY - 2019
SP - 240
EP - 248
DO - 10.5220/0007361902400248

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