Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 107-113.doi: 10.6040/j.issn.1672-3961.0.2017.385

• Electrical Engineering • Previous Articles     Next Articles

Algorithm of underwater target recognition based on CNN features with BOF

Wenwen QUAN(),Mingxing LIN*()   

  1. School of Mechanical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2017-08-03 Online:2019-02-01 Published:2019-03-01
  • Contact: Mingxing LIN E-mail:qw13541179302@126.com;mxlin2000@163.com

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Abstract:

In order to prevent false matching problems of scale invariant feature transform (SIFT) matching as a low-level representation for lack of sufficient features, an improved bag of features (BOF) algorithm method combined with the convolution neural network (CNN) features was proposed, which had better semantic segmentation ability to enhance the recognition rates. The LifeCLEF fish video on ImageCLEF website was used to create our own target image databases. Convolution neural network was trained in the Alexnet architecture of caffe, and the features of image databases and query images were extracted. The trained CNN features were simulated in Matlab, and the hamming distance was calculated to verify the matching effect. In addition, the parameter values were changed to test the effect of different Hamming distance thresholds on target recognition results. The experiment of self-made image databases showed that the fusion of depth learning features could effectively improve the underwater target recognition rates of BOF algorithm, and the selection of Hamming distance thresholds required selecting the appropriate parameters according to the actual situation.

Key words: underwater target recognition, bag of features, scale invariant feature transform matching, convolution neural networks, Hamming distance

CLC Number: 

  • TP391

Fig.1

The framework of underwater target recognition method with CNN features"

Fig.2

Fish species in new image databases"

Table 1

The number of images for training, validation and testing"

类别 训练 验证 测试 训练验证总计
短身光鳃雀鲷
弓月蝴蝶鱼
黑带椒雀鲷
三带圆雀鲷
网纹圆雀鲷
小高鳍刺尾鱼		 2 310
926
2 300
1 516
2 303
650		 289
116
288
190
288
82		 288
116
288
189
288
82		 2 599
1 042
2 588
1 706
2 591
732
总计 10 005 1 253 1 251 11 258

Table 2

The recognition rates of six kinds of fish (T=0.600)"

%
方法 BOF CNN BOF+Conv5 BOF+Fc6 BOF+Fc7
短身光鳃雀鲷
弓月蝴蝶鱼
黑带椒雀鲷
三带圆雀鲷
网纹圆雀鲷
小高鳍刺尾鱼		 74.0
70.7
71.6
76.5
69.7
69.5		 74.3
73.8
72.6
75.8
74.2
72.4		 86.8
85.5
83.1
87.3
82.8
82.1		 86.7
85.3
86.2
87.0
84.5
84.8		 76.0
72.8
74.6
76.8
71.3
74.2
平均识别率 72.2 73.9 84.7 85.9 74.3

Fig.3

Distribution of hamming distance"

Fig.4

The impact of parameter T on recognition rates"

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