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Official Journal of the Japan Wood Research Society

Table 3 Comparison of recognition performance

From: Evaluation of image partitioning strategies for preserving spatial information of cross-sectional micrographs in automated wood recognition of Fagaceae

Strategy Parameters Dimensionality F1 score
SIFT nOctaveLayers = 3, contrastThreshold = 0.06, edgeThreshold = 10, sigma = 1.6 (as default) 128 0.649
BOF k = 300 300 0.708
SPM Pyramid level 1 1500 0.722
RSPM Partition level 2 900 0.738
TSPM Partition level 0 (=BOF) 300 0.708
RSPM + AC R2SPM + AC at partition level 7 (k = 18) 1044 0.742
RSPM + PC R2SPM + PC at partition level 9 (k = 18) 1053 0.746
RSPM + AC + PC R2SPM + AC and PC at partition level 8 (k = 18) 1215 0.750
VGG16 Optimizer = SGD, learning rate = 0.01 0.705
  1. SIFT, scale-invariant feature transform; nOctaveLayers, the number of layers in each octave; contrastThreshold, the contrast threshold used to filter out weak features; edgeThreshold, the threshold used to filter out edge-like features; sigma, the sigma of Gaussian applied to the image at the octave number 0; BOF, bag-of-features; k, number of codewords; SPM, spatial pyramid matching; RSPM, radial-SPM; R2SPM, RSPM with partition level 2; TSPM, tangential-SPM; AC, autocorrelation; PC, Pearson correlation coefficient; SGD, stochastic gradient descent