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

RichHTML

7

PDF (PC)

478

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"

1 JAFFE J S , MOORE K D , MCLEAN J , et al. Underwater optical imaging: status and prospects[J]. Oceanography, 2001, 14 (3): 66- 76.
2 王士龙, 徐玉如, 万磊, 等. 基于边界矩和改进FCM聚类的水下目标识别[J]. 系统工程理论与实践, 2012, 32 (12): 2809- 2815.
WANG Shilong , XU Yuru , WANG Lei , et al. Underwater targets recognition based on contour moment and modified FCM algorithm[J]. System Engineering Theory and Practice, 2012, 32 (12): 2809- 2815.
3 FATAN M , DALIRI M R , MOHAMMAD S A , et al. Underwater cable detection in the images using edge classification based on texture information[J]. Measurement, 2016, 91, 309- 317.
doi: 10.1016/j.measurement.2016.05.030
4 乔曦.基于水下机器视觉的海参实时识别研究[D].北京:中国农业大学, 2017.
QIAO Xi. Sea cucumber identification in real-time based on underwater machine vision technique[D]. Beijing: China Agricultural University, 2017.
5 LOWE D G . Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2004, 60 (2): 91- 110.
doi: 10.1023/B:VISI.0000029664.99615.94
6 BAY H, TUYTELAARS T, VAN GOOL L. SURF: speeded up robust features[C]//Computer Vision-ECCV 2006. Graz, Austria: Springer Berlin Heidelberg, 2006: 404-417.
7 ZHENG Z Z , YUN Z , YAN L X . Global and local exploitation for saliency using bag-of-words[J]. IET Computer Vision, 2014, 8 (4): 299- 304.
doi: 10.1049/iet-cvi.2013.0132
8 KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Proceedings of the Conference on Neural Information Processing Systems. Lake Tahoe, Spain: IEEE, 2012: 1097-1105.
9 REN S , HE K , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137- 1149.
doi: 10.1109/TPAMI.2016.2577031
10 LECUN Y , BOTTOU L , BENGIO Y , et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86 (11): 2278- 2324.
doi: 10.1109/5.726791
11 SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 1-9.
12 HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//CVPR 16: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Los Vegas, USA: IEEE, 2016: 770-778.
13 LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 1337-1342.
14 EKANAYAKE J, PALLICKARE S. Map reduce for data intensive scientific analysis[C]//IEEE Science. Piscatway, USA: IEEE, 2008: 277-284.
15 LAZEBNIK S, SCHMID C, PONCE J. Video google: a text retrieval approach to object matching in videos[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006: 2169-2178.
16 SIVIC J, ZISSERMAN A. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories[C]//Proceedings of Ninth IEEE International Conference on Computer Vision. Nice, France: IEEE, 2003: 1470-1477.
17 JEGOU H , DOYZE M , SCHMID C . Improving bag of features for large scale image search[J]. International Journal of Computer Vision, 2010, 87 (3): 316- 336.
doi: 10.1007/s11263-009-0285-2
18 ZHANG G X , ZENG Z , ZHANG S W , et al. SIFT matching with CNN evidences for particular object retrieval[J]. Neurocomputing, 2017, 238, 399- 409.
doi: 10.1016/j.neucom.2017.01.081
19 RUSSAKOVSKY O , DENG J , SU H , et al. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2014, 115 (3): 211- 252.
20 CAMPBELL A T , MEER H G D , KOUNAVIS M E , et al. A survey of programmable networks[J]. ACM Computer Communications Review, 1999, 29 (2): 7- 23.
doi: 10.1145/505733
21 LI X, SHANG M, QIN H W, et al. Fast Accurate Fish detection and recognition of Underwater Images with Fast R-CNN[C]//Oceans. Washington, USA: IEEE, 2015: 1-5.
22 JIA Y, SHELHAMER E, DONAHUE J, et al. Caffe: convolutional architecture for fast feature embedding[C]//Proceedings of the 22nd ACM international conference on Multimedia. Orlando, USA: arXiv, 2014: 675-678.
[1] Jun FAN,Qiaolin YE,Ning YE. Face recognition based on improved prameter-free supervised localitypreserving projections [J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 10-16.
[2] Yunyang YAN,Huizhen ZHANG,Yi′an LIU,Shangbing GAO. Video flame detection based on GMM and 3D-LBP feature [J]. Journal of Shandong University(Engineering Science), 2019, 49(1): 1-9.
[3] Qiyue SONG,Xuewen MU,Huan CHENG. Segmentation of connected characters based on improved drop-fall algorithm [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 89-94, 108.
[4] Fang LIU,Guangchao WU. An improved Apriori algorithm based on compression matrix [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 82-88.
[5] Yingxue ZHU,Ruizhang HUANG,Can MA. A short text dynamic clustering approach bias on new topic [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 8-18.
[6] Cui JIN,Hongyuan WANG,Shoubing CHEN. Person re-identification based on random erasing pedestrian alignmentnetwork method [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 67-73.
[7] Xing LI,Zhenjie HOU,Jiuzhen LIANG,Xingzhi CHANG. Multi-node human behavior recognition based on linear acceleration [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 56-66.
[8] Yao LI,Zhihai WANG,Yan′ge SUN,Wei ZHANG. An adaptive ensemble classification method based on deep attribute weighting for data stream [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 44-55, 66.
[9] Qingtao QU,Qicheng LIU,Chunxiao MU. A parallel adaptive news topic tracking algorithm based on N-Gram language model [J]. Journal of Shandong University(Engineering Science), 2018, 48(6): 37-43.
[10] XIONG Bingyan, WANG Guoyin, DENG Weibin. Hierarchical cost sensitive decision tree and its application in the prediction of the mobile phone replacement [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 0, (): 36-42.
[11] WANG Xiaochu, WANG Shitong, BAO Fang. Image classification algorithm based on minimax probability machine with regularized probability density concensus [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 0, (): 13-21.
[12] Dongdong SHEN,Fengyu ZHOU,Mengyuan LI,Shuqian WANG,Renhe GUO. Indoor wireless positioning based on ensemble deep neural network [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 95-102.
[13] Jianping HU,Xin LI,Qi XIE,Ling LI,Daochang ZHANG. An unconstrained optimization EMD approach in 2D based on Delaunay triangulation [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 9-15, 37.
[14] Chenmou WU,Zhijun FANG,Jenqneng HWANG. Active driving behavior analysis algorithm based on monocular camera [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 69-76.
[15] Guoxin WANG,Fengdong CHEN,Guodong LIU. Feature extraction method of color pseudo-random coded structured light [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 55-60.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] XIA Bin,ZHANG Lian-jun . Energy comparison-based TOA estimation algorithm for the DS-CDMA UWB system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(1): 70 -73 .
[2] BO De-Yun, ZHANG Dao-Jiang. Adaptive spectral clustering algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 22 -26 .
[3] ZHAO Ke-Jun, WANG Xin-Jun, LIU Xiang, CHOU Yi-Hong. Algorithms of continuous top-k join query over structured overlay networks[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 32 -37 .
[4] DING Wan-Tao, LI Shu-Cai, ZHANG Qing-Song. Discussion on interface error regularity of inclined  stratum predicted by TSP[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(4): 57 -60 .
[5] WANG Bai-wei,CAO Sheng-le . A mult-objective assessment method of the effects of industrial waste-water management[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(3): 89 -92 .
[6] CHOU Wu-Sheng, WANG Shuo. Study on the adaptive algorithm of the force reflection robotic master under large stiffness of the environment[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(1): 1 -5 .
[7] ZHANG Hui，WANG Meng-xia, HAN Xue-shan. The advanced thermal rating of power system and its application[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(6): 25 -29 .
[8] YAN Chong-jing, LIAO Wen-he, GUO Yu, CHENG Xiao-sheng. The BOM modeling based on the polychromatic graph[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(6): 70 -75 .
[9] WANG Jian-ping,WANG Shu-hua,GENG Gui-li . Study on the transformation activation energy in InN semiconductor nanocrystals[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(2): 42 -44 .
[10] CHEN Bin, CHEN Song-Can, PAN Zhi-Song, LI Bin. Survey of outlier detection technologies[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(6): 13 -23 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd