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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