Texture Images Classification Algorithm Combining Wavelet Transform and Capsule Network

Layer	Output size	DWTCapsNet
Conv	64×64	kernel size: 7×7, stride: 2, with padding
DWT	32×32	wavelet decomposition
Dense block_1	32×32	$[\begin{array}{l} 1 \times 1 Conv \\ 3 \times 3 Conv \end{array}]$ ×6
Transition	32×32	kernel size: 1×1, stride: 1
Transition	16×16	2×2max pooling, stride: 2
Dense block_2	16×16	$[\begin{array}{l} 1 \times 1 Conv \\ 3 \times 3 Conv \end{array}]$ ×12
Reduce Channel	16×16	kernel size: 3×3, stride: 1, with padding
PrimaryCaps	pose: 16×16×128 activation: 16×16×8	kernel size: 1×1, stride: 1
ConvCaps	pose: 7×7×256activation: 7×7×16	kernel size: 3×3, stride: 2
Class Capsule	pose: E×16activation: E	kernel size: 1×1, stride: 1

Table 2. Division of different data sets
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Table 2. Division of different data sets
Data set KTH KTD UIUC
Train set 3564 3136 700
Test set 1188 1344 300
Class 11 28 25
Type color gray gray
Total number of images 4752 4480 1000

Table 3. Classification accuracies of different texture classification algorithms

View table

Table 3. Classification accuracies of different texture classification algorithms

Type	Model	KTH /%	KTD /%	UIUC /%	Reference
Traditional algorithm	LBP	89.3	97.8	88.1	Ref. [24]
	ILBP	96.9	99.7	91.9	Ref. [25]
	FLBP	94.3	99.2	91.3	Ref. [26]
	GPBMFD	--	--	90.30	Ref. [27]
	LQC	96.39	--	92.62	Ref. [28]
	SLGP	95.60	--	--	Ref. [29]
	LCCMSP	93.32	99.69	--	Ref. [30]
	LQPAT	83.76	96.92	--	Ref. [31]
CNN-based algorithm	T-CNN-3	99.4	73.2	--	Ref. [32]
	VisualNet	72.4	97.8	--	Ref. [33]
	VGG-VD-16	97.8	99.5	93.3	Ref. [11]
	Deep-TEN	84.5	--	--	Ref. [34]
	LFV	82.6	--	--	Ref. [35]
DWTCapsNet		99.92	99.79	91.74	ours

Table 4. Classification accuracies of DWTCapsNet on color and gray texture images unit: %
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Table 4. Classification accuracies of DWTCapsNet on color and gray texture images unit: %
Data set KTH-color KTH-gray
Accuracy 99.92 96.88

Table 5. Classification result of DWTDenseNet unit: %
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Table 5. Classification result of DWTDenseNet unit: %
Data set KTH KTD UIUC
DenseNet 98.20 99.66 87.07
DWTDenseNet 99.38 99.72 87.83

Table 6. Classification result of DenseCapsNet unit: %
View table
Table 6. Classification result of DenseCapsNet unit: %
Data set KTH KTD UIUC
MatrixCapsNet 93.18 84.73 45.31
DenseCapsNet 93.91 99.01 88.12

Table 7. Classification accuracy of wavelet capsule network on rotating image data set
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Table 7. Classification accuracy of wavelet capsule network on rotating image data set
Rotation angle /(°) Accuracy /% Rotation angle /(°) Accuracy /% Rotation angle /(°) Accuracy /%
0 99.71 120 41.26 240 41.26
30 67.25 150 57.10 270 50.99
60 44.60 180 96.66 300 43.11
90 51.27 210 64.27 330 62.00

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Zhiyong Tao, jie Li, Xiaoliang Tang. Texture Images Classification Algorithm Combining Wavelet Transform and Capsule Network[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241002

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Paper Information

Category: Image Processing

Received: Apr. 21, 2020

Accepted: May. 22, 2020

Published Online: Dec. 2, 2020

The Author Email: Li jie (leej95@163.com)

DOI:10.3788/LOP57.241002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Structural parameter of wavelet capsule network

Table 1. Structural parameter of wavelet capsule network

Table 2. Division of different data sets

Table 2. Division of different data sets

Table 3. Classification accuracies of different texture classification algorithms

Table 3. Classification accuracies of different texture classification algorithms

Table 4. Classification accuracies of DWTCapsNet on color and gray texture images unit: %

Table 4. Classification accuracies of DWTCapsNet on color and gray texture images unit: %

Table 5. Classification result of DWTDenseNet unit: %

Table 5. Classification result of DWTDenseNet unit: %

Table 6. Classification result of DenseCapsNet unit: %

Table 6. Classification result of DenseCapsNet unit: %

Table 7. Classification accuracy of wavelet capsule network on rotating image data set

Table 7. Classification accuracy of wavelet capsule network on rotating image data set

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