Fast Image Matching Based on Channel Attention and Feature Slicing

Shaoyan Gai; Yanyan Huang; Feipeng Da

doi:10.3788/AOS231054

Acta Optica Sinica, Volume. 43, Issue 22, 2210001(2023)

Fast Image Matching Based on Channel Attention and Feature Slicing

Shaoyan Gai^1,2、*, Yanyan Huang^1,2, and Feipeng Da^1,2、**

Author Affiliations

¹School of Automation, Southeast University, Nanjing 210096, Jiangsu , China

²Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, Jiangsu , China

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Figures & Tables(15)

Fig. 1. Processing flow of fast image matching algorithm based on channel attention and feature slicing

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Fig. 2. Structure of GFDM

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Fig. 3. Slice coding (left) and direct coding (right)

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Fig. 4. Structure of SEM

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Fig. 5. Structure of CAFSD

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Fig. 6. Visualization results of CAFSD (left) and CDbin (right). (a) 45° rotation; (b) 90° rotation; (c) 135° rotation; (d) 0.3 diminution+135° rotation

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Table 1. FPR95 under different coefficients on UBC Phototour dataset
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Table 1. FPR95 under different coefficients on UBC Phototour dataset
Weight L_Q L_E L_C
1∶1 2.42 2.66 2.95
1∶0.5 2.50 2.55 2.89
1∶0.1 2.58 2.63 3.01

Table 2. FPR95 before and after modifying weight coefficient on UBC Phototour dataset
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Table 2. FPR95 before and after modifying weight coefficient on UBC Phototour dataset
1∶α∶β∶γ FPR95 /%
1∶1∶1 ∶1 2.72
1∶1∶0.5∶0.5 2.65

Table 3. Results before and after modifying weight coefficient on circuit board dataset
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Table 3. Results before and after modifying weight coefficient on circuit board dataset
1∶α∶β∶γ Matching accuracy Score Time /s
1∶1∶1 ∶1 0.9113 0.6161 2.57
1∶1∶0.5∶0.5 0.9396 0.6230 2.58

Table 4. FPR95 of different modules on UBC Phototour dataset
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Table 4. FPR95 of different modules on UBC Phototour dataset
Module FPR95/%
CDbin^［7］ 3.89
GFDM 2.78
SEM 2.89
both+L_T 2.76
both+L_all 2.65

Table 5. Results of different modules on circuit board dataset
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Table 5. Results of different modules on circuit board dataset
Module Matching accuracy Score Time /s
CDbin^［7］ 0.8950 0.6097 2.173
GFDM 0.9085 0.6155 2.12
SEM 0.9080 0.6127 2.39
both+L_T 0.9101 0.6149 2.58
both+L_all 0.9396 0.6230 2.58

Table 6. FPR95 of different algorithms on UBC Phototour dataset
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Table 6. FPR95 of different algorithms on UBC Phototour dataset
Algorithm FPR95 /%
ORB^［4］ 57.54
CDbin^［7］ 3.89
BLCD^［12］ 8.52
DBLD^［15］ 2.69
DCBMQ^［21］ 31.51
CFBD^［17］ 2.97
LDBD^［22］ 19.83
CAFSD 2.65

Table 7. Results of different algorithms on circuit board dataset

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Table 7. Results of different algorithms on circuit board dataset

Algorithm	Matching accuracy	Score	Time/s
ORB^［4］	0.8160	0.6051	0.226
CDbin^［7］	0.8950	0.6097	2.173
BLCD^［12］	0.9018	0.6130	2.67
DBLD^［15］	0.9215	0.6011	3.89
DCBMQ^［21］	0.8520	0.6013	2.60
CFBD^［17］	0.9173	0.6163	2.81
LDBD^［22］	0.8991	0.6107	3.68
CAFSD	0.9396	0.6230	2.58

Table 8. Results on DAISY dataset

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Table 8. Results on DAISY dataset

Algorithm	Matching accuracy	Score	Time /s
ORB^［4］	0.8310	0.5146	0.115
CDbin^［7］	0.8874	0.6081	1.258
BLCD^［12］	0.9071	0.5960	1.498
DBLD^［15］	0.9184	0.6081	2.096
DCBMQ^［21］	0.8962	0.5973	1.870
CFBD^［17］	0.9050	0.6099	1.711
LDBD^［22］	0.8873	0.6001	2.582
CAFSD	0.9199	0.6129	1.880

Table 9. Results on RetinaMnist dataset

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Table 9. Results on RetinaMnist dataset

Algorithm	Matching accuracy	Score	Time /s
ORB^［4］	0.8709	0.5830	0.099
CDbin^［7］	0.9131	0.6291	1.145
BLCD^［12］	0.9189	0.6302	1.335
DBLD^［15］	0.9210	0.6289	1.995
DCBMQ^［21］	0.9037	0.6129	1.617
CFBD^［17］	0.9150	0.6219	1.610
LDBD^［22］	0.9047	0.6121	1.895
CAFSD	0.9385	0.6316	1.615

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Shaoyan Gai, Yanyan Huang, Feipeng Da. Fast Image Matching Based on Channel Attention and Feature Slicing[J]. Acta Optica Sinica, 2023, 43(22): 2210001

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