Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Table 5 Performances of k-NN, SVM, and ANN classifiers reported in CV-based wood identification studies

References	Database	Image type	#SP/#IMG	Feature	Classification rate (%)
References	Database	Image type	#SP/#IMG	Feature	k-NN	SVM^a	ANN
Hu et al. [128]	–	Macro	28/2800	SIFT	77.3	87.5	90.2
Tou et al. [117]	CAIRO	Stereo	5/500	GLCM	63.6	–	72.8
Prasetiyo et al. [147]	FRIM	Stereo	25/2390	LBP	80.0	85.6	93.6
Wang et al. [148]	ZAFU WS 24	Stereo	24/480	GLCM	87.5	91.7	–
Wang et al. [68]	ZAFU WS 24	Stereo	24/481	HLAC with MMI	76.3	87.7	–
Souza et al. [52]	–	Stereo	64/1901	LBP	–	98.1	96.5
Yadav et al. [192]	UFPR	Micro	25/500	1st Stat. with Coiflet DWT	–	65.2	92.2
Hwang et al. [73]	XDD	Micro	39/1557	SIFT	84.3	95.4	–
Kobayashi et al. [48]	XDD	Micro	18/2406	SIFT, CC	95.3	93.1	–

#SP number of species, #IMG number of images, k-NN k-nearest neighbors, SVM support vector machine, ANN artificial neural network, Stereo stereogram, Micro micrograph, Macro macroscopic image, MMI mask matching image, 1st Stat first order statistic features, DWT discrete wavelet transform, CC connected component labelling
^aLinear kernel SVM classifier

ISSN: 1746-4811