Efficient Bark Recognition in the Wild

• DOI: 10.5220/0007361902400248
• Publication Date: 2019-03-15T11:03:30Z
• Authors: Rémi Ratajczak; Sarah Bertrand; Carlos Crispim-Ju...; Laure Tougne
• Keywords: Bark recognition; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; 02 engineering and technology; 15. Life on land; Data Fusion; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; 0202 electrical engineering, electronic engineering, information engineering; Features Extraction; texture classification; Color and Texture Analyses; color quantification; Dimensionality Reduction

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/