@article {62494,
	title = {Tree trunk texture classification using multi-scale statistical macro binary patterns and CNN},
	journal = {Applied Soft Computing},
	volume = {118},
	year = {2022},
	month = {2022},
	pages = {108473},
	abstract = {
	Automated plant\&nbsp;classification\&nbsp;using tree trunk has attracted increasing interest in the\&nbsp;computer vision\&nbsp;community as a contributed solution for the management of biodiversity. It is based on the description of the texture information of the bark surface. The multi-scale variants of the\&nbsp;local binary patterns\&nbsp;have achieved prominent performance in bark texture description. However, these approaches encode the scale levels of the macrostructure separately from each other. In this paper, a novel handcrafted\&nbsp;texture descriptor\&nbsp;termed multi-scale Statistical Macro Binary Patterns (ms-SMBP) is proposed to encode the characterizing macro pattern of different bark species. The proposed approach consists of defining a sampling scheme at high scale levels and summarizing the intensity distribution using statistical measures. The characterizing macro pattern is encoded by an in-depth gradient that describes the relationship between the scale levels and their adaptive statistical prototype. Besides this handcrafted feature descriptor, a learning-based description is performed with the ResNet34 model for bark classification. Extensive and comprehensive experiments on challenging and large-scale bark datasets demonstrate the effectiveness of ms-SMBP to identify bark species and outperforming different multi-scale LBP approaches. The tree trunk classification with ResNet34 shows interesting results on a very large-scale dataset.

},
	isbn = {1568-4946},
	url = {https://doi.org/10.1016/j.asoc.2022.108473},
	author = {Boudra, Safia and Yahiaoui, Itheri and Behloul, Ali}
}
