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Title: Automated identification of Lauraceae by scale-invariant feature transform
Authors: Hwang, Sung-Wook
Kobayashi, Kayoko
Zhai, Shengcheng
Sugiyama, Junji  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 小林, 加代子
杉山, 淳司
Keywords: Classification
Computer vision
Hierarchical clustering
Linear discriminant analysis
Optical micrograph
Issue Date: Apr-2018
Publisher: Springer Nature
Journal title: Journal of Wood Science
Volume: 64
Issue: 2
Start page: 69
End page: 77
Abstract: An image dataset of the cross-sectional optical micrographs of the Lauraceae species including 39 species in 11 genera, capturing at least one full annual ring, was investigated by scale-invariant feature transform (SIFT), a computer vision-based feature extraction algorithm. We found an image of 900 × 900-pixel size at a pixel resolution of ca. 3 µm, corresponding to the actual size of 2.65 × 2.65 mm2, as the minimum requirement for the image dataset in terms of the accuracy of the recognition at both the genus and species levels. Among the several classifiers investigated, the linear discriminant analysis (LDA) presented the best performance reaching a maximum of 89.4 % in the genus with a species identification of approximately 96.3%. Cluster analysis of all the SIFT descriptors for each image yielded practical information regarding the descriptors; they recognize selectively the cell lumina, cell corners, vessels, and axial and ray parenchyma cells. Therefore, the difference between the genus or species levels was determined per the variation in the quantities of these computer-based properties. Another clustering approach, the hierarchal dendrogram, was applied to visualize the numerical distances between the genus and species. Interestingly, even Machilus and Phoebe, which are difficult to distinguish by conventional visual inspection, are quite distantly classified at the genus level. In contrast, some species in Cinnmamomum, Machilus and Litsea were categorized into different subgroups rather than the original genus. Microscopic wood identification is found to be possible at the genus level; however, the numerical dataset of the morphological features has various overlapping clusters, causing the genus-level identification of the Lauraceae to be more difficult than species-level identification.
Rights: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
DOI(Published Version): 10.1007/s10086-017-1680-x
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