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
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.