Although wood cross sections contain spatiotemporal information regarding tree growth, computer vision-based wood identification studies have traditionally favored disordered image representations that do not take such information into account. This paper describes image partitioning strategies that preserve the spatial information of wood cross-sectional images. Three partitioning strategies are designed, namely grid partitioning based on spatial pyramid matching and its variants, radial and tangential partitioning, and their recognition performance is evaluated for the Fagaceae micrograph dataset. The grid and radial partitioning strategies achieve better recognition performance than the bag-of-features model that constitutes their underlying framework. Radial partitioning, which is a strategy for preserving spatial information from pith to bark, further improves the performance, especially for radial-porous species. The Pearson correlation and autocorrelation coefficients produced from radially partitioned sub-images have the potential to be used as auxiliaries in the construction of multi-feature datasets. The contribution of image partitioning strategies is found to be limited to species recognition and is unremarkable at the genus level.

中文翻译：

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图像分类策略的评估，以保留断面显微照片的空间信息，以自动识别菊科植物

尽管木材横截面包含有关树木生长的时空信息，但基于计算机视觉的木材识别研究传统上倾向于不考虑此类信息的无序图像表示。本文介绍了保留木材横截面图像空间信息的图像分割策略。设计了三种分区策略，即基于空间金字塔匹配及其变体的网格分区，径向和切向分区，并针对Fagaceae显微照片数据集评估了它们的识别性能。与构成其基础框架的功能包模型相比，网格和径向分区策略可实现更好的识别性能。径向分区是一种从髓到树皮保留空间信息的策略，进一步提高了性能，尤其是对于径向多孔物种。从径向分割的子图像产生的皮尔逊相关系数和自相关系数有可能在构建多特征数据集时用作辅助工具。发现图像分割策略的贡献仅限于物种识别，并且在属水平上没有显着影响。