Efficient Bark Recognition in the Wild - Archive ouverte HAL Access content directly
Conference Papers Year :

Efficient Bark Recognition in the Wild

(1, 2, 3) , (1) , (1) , (1)
1
2
3

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 (http://eidolon.univ-lyon2.fr/~remi1/Bark-101/), a new challenging dataset including manually segmented images of 101 bark classes that we release publicly. Bark-101: http://eidolon.univ-lyon2.fr/~remi1/Bark-101/
Fichier principal
Vignette du fichier
ratajczak2019-visapp.pdf (3.33 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02022629 , version 1 (17-03-2019)

Identifiers

Cite

Rémi Ratajczak, Sarah Bertrand, Carlos F 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⟩
585 View
164 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More