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Efficient Bark Recognition in the Wild

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. Bark-101:
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Submitted on : Sunday, March 17, 2019 - 7:00:36 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM
Long-term archiving on: : Tuesday, June 18, 2019 - 12:23:50 PM


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Rémi Ratajczak, Sarah Bertrand, Carlos Crispim-Junior, Laure Tougne. Efficient Bark Recognition in the Wild. International Conference on Computer Vision Theory and Applications (VISAPP 2019), Feb 2019, Prague, Czech Republic. ⟨10.5220/0007361902400248⟩. ⟨hal-02022629⟩



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