Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 353-357.doi: 10.11896/jsjkx.210200169

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure

YANG Jian-nan1, ZHANG Fan2   

  1. 1 Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China
    2 IBM Watson Group,Littleton,MA 01460,USA
  • Online:2022-06-10 Published:2022-06-08
  • About author:YANG Jian-nan,born in 1995,postgraduate.His main research interests include image recognition and computer vision.

Abstract: The image recognition of small crops is very challenging for several reasons.First,the crop is small in size and a single sample is not representative of a collection.Second,different categories or different grades of the same crop may look very similar in shapes and colors.At present,there is a lack of research on image classification methods for small crops such as dried tea,rice and soybean,and most of the research datasets are taken in the laboratory environment with professional equipment,which brings difficulties to the practical application.For this,a method for image acquisition and processing of small crop samples using mobile phones is proposed.By taking tea and rice as a case study,we design a hierarchical network structure combined with two attention mechanisms.Through the coarse-grained to fine-grained classification process,coarse-grained classification is made first,namely different categories of samples,and then combined with two attention mechanisms,the network pays more attention to the diffe-rences between similar samples of different grades under the same category,so that they can be more accurate to classification of samples.Finally,the proposed method achieves the accuracy of 93.9% on the collected datasets.

Key words: Attention mechanism, Convolutional neural network, Hierarchical network structure, Image classification, Small crops

CLC Number: 

  • TP301.6
[1] LI S,ZHOU K,CHENG W Q,et al.Research progress of teaquality monitoring based on image technology[J].Science and Technology of Modern Agriculture,2019,736(2):202-204,208.
[2] XU M,WANG J,GU S.Rapid identification of tea quality byE-nose and computer vision combining with a synergetic data fusion strategy[J].Journal of Food Engineering,2019,241:10-17.
[3] LIU P,WU K M,YANG P X,et al.Study of sensory qualityevaluation of tea using computer vision technology and forest random method[J].Spectroscopy and Spectral Analysis,2019,39(1):193-198.
[4] YU H,WU R M,AI S R,et al.Study on computer vision classification of tea quality based on PCA-PSO-LSSVM[J].Laser Journal,2017,1:55-58.
[5] SONG Y,XIE H,NING J,et al.Grading Keemun black teabased on shape feature parameters of machine vision[J].Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering,2018,34(23):279-286.
[6] IZQUIERDO M,LASTRA-MEJÍAS M,GONZÁLEZ-FLORES E,et al.Visible imaging to convolutionally discern and authenticate varieties of rice and their derived flours[J].Food Control,2020,110:106971.
[7] TOUSCH A M,HERBIN S,AUDIBERT J Y.Semantic hierarchies for image annotation:A survey[J].Pattern Recognition,2012,45(1):333-345.
[8] GAO T,KOLLER D.Discriminative learning of relaxed hierarchy for large-scale visual recognition[C]//2011 International Conference on Computer Vision.IEEE,2011:2072-2079.
[9] DENG J,KRAUSE J,BERG A C,et al.Hedging your bets:Optimizing accuracy-specificity trade-offs in large scale visual re-cognition[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:3450-3457.
[10] LIU B,SADEGHI F,TAPPEN M,et al.Probabilistic label trees for efficient large scale image classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:843-850.
[11] XU M,WANG J,GU S.Rapid identification of tea quality byE-nose and computer vision combining with a synergetic data fusion strategy[J].Journal of Food Engineering,2018,241:10-17.
[12] AUKKAPINYO K,SAWANGWONG S,POOYOI P,et al.Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network[J].Internatio-nal Journal of Automation & Computing,2020(2):233-246.
[13] SON N H,THAI N.Deep Learning for Rice Quality Classification[C]//2019 International Conference on Advanced Computing and Applications (ACOMP).2019.
[14] SONG Y,XIE H F,NING J M,et al.Grade identification of qimen black tea based on machine visual shape feature parameters[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(23):279-286.
[15] IZQUIERDO M,MIGUEL L M,ESTER G F,et al.Visible imaging to convolutionally discern and authenticate varieties of rice and their derived flours[J].Food Control,2019,110:106971.
[16] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[17] CAO Y,XU J,LIN S,et al.GCNet:Non-local Networks Meet Squeeze-Excitation Networks and Beyond[C]//IEEE/CVF International Conference on Computer Vision Workshop.2019:1971-1980.
[18] WANG X,GIRSHICK R,GUPTA A,et al.Non-local neuralnetworks[C]//Proceedings of the IEEE Conference on Compu-ter Vision and Pattern Recognition.2018:7794-7803.
[19] HU J,SHEN L,SUN G,et al.Squeeze-and-Excitation Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(8):2011-2023.
[20] HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[21] LIN T Y,ROYCHOWDHURY A,MAJI S.Bilinear cnn models for fine-grained visual recognition[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1449-1457.
[22] FU J,ZHENG H,MEI T.Look closer to see better:Recurrent attention convolutional neural network for fine-grained image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4438-4446.
[23] MOGHIMI M,BELONGIE S J,SABERIAN M J,et al.Boosted Convolutional Neural Networks[C]//BMVC.2016.
[24] ZHENG H,FU J,MEI T,et al.Learning multi-attention convolutional neural network for fine-grained image recognition[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:5209-5217.
[25] YANG Z,LUO T,WANG D,et al.Learning to navigate forfine-grained classification[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:420-435.
[26] ZHENG H,FU J,ZHA Z J,et al.Looking for the devil in the details:Learning trilinear attention sampling network for fine-grained image recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:5012-5021.
[1] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[2] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[3] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[4] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[5] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[6] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[7] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[8] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[10] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[11] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[12] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[13] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[14] XIONG Luo-geng, ZHENG Shang, ZOU Hai-tao, YU Hua-long, GAO Shang. Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism [J]. Computer Science, 2022, 49(7): 212-219.
[15] PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun. Satellite Onboard Observation Task Planning Based on Attention Neural Network [J]. Computer Science, 2022, 49(7): 242-247.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!