A Multi-Feature Fusion Based on Transfer Learning for Chicken Embryo Eggs Classification

被引:13
|
作者
Huang, Lvwen [1 ,2 ,3 ]
He, Along [1 ]
Zhai, Mengqun [4 ]
Wang, Yuxi [1 ]
Bai, Ruige [1 ]
Nie, Xiaolin [1 ]
机构
[1] NorthWest A&F Univ, Coll Informat Engn, Yangling 712100, Shaanxi, Peoples R China
[2] Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China
[3] Shaanxi Key Lab Agr Informat Percept & Intelligen, Yangling 712100, Shaanxi, Peoples R China
[4] NorthWest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 05期
关键词
Transfer learning; deep feature; SPF; embryo; SURF; HOG; DCNN; agriculture; FERTILE EGGS; DEEP; IDENTIFICATION; MACHINE; VISION;
D O I
10.3390/sym11050606
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The fertility detection of Specific Pathogen Free (SPF) chicken embryo eggs in vaccine preparation is a challenging task due to the high similarity among six kinds of hatching embryos (weak, hemolytic, crack, infected, infertile, and fertile). This paper firstly analyzes two classification difficulties of feature similarity with subtle variations on six kinds of five- to seven-day embryos, and proposes a novel multi-feature fusion based on Deep Convolutional Neural Network (DCNN) architecture in a small dataset. To avoid overfitting, data augmentation is employed to generate enough training images after the Region of Interest (ROI) of original images are cropped. Then, all the augmented ROI images are fed into pretrained AlexNet and GoogLeNet to learn the discriminative deep features by transfer learning, respectively. After the local features of Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradient (HOG) are extracted, the multi-feature fusion with deep features and local features is implemented. Finally, the Support Vector Machine (SVM) is trained with the fused features. The verified experiments show that this proposed method achieves an average classification accuracy rate of 98.4%, and that the proposed transfer learning has superior generalization and better classification performance for small-scale agricultural image samples.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] HSRRS Classification Method Based on Deep Transfer Learning And Multi-Feature Fusion
    Wang, Ziteng
    Li, Zhaojie
    Wang, Yu
    Li, Wenmei
    Yang, Jie
    Ohtsuki, Tomoaki
    2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
  • [2] INTERNET TOURISM SCENE CLASSIFICATION WITH MULTI-FEATURE FUSION AND TRANSFER LEARNING
    Liu, Jie
    Du, Junping
    Wang, Xiaoru
    PROCEEDINGS OF 2011 INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY AND APPLICATION, ICCTA2011, 2011, : 747 - 751
  • [3] Defect identification of wind turbine blade based on multi-feature fusion residual network and transfer learning
    Zhu, Jiawei
    Wen, Chuanbo
    Liu, Jihui
    ENERGY SCIENCE & ENGINEERING, 2022, 10 (01) : 219 - 229
  • [4] Classification of Thyroid Standard Planes in Ultrasound Images based on Multi-feature Fusion
    Wang, Jing
    Liu, Peizhong
    PROCEEDINGS OF 2019 IEEE 13TH INTERNATIONAL CONFERENCE ON ANTI-COUNTERFEITING, SECURITY, AND IDENTIFICATION (IEEE-ASID'2019), 2019, : 75 - 79
  • [5] Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network
    Yang, Lei
    Fan, Junfeng
    Huo, Benyan
    Liu, Yanhong
    JOURNAL OF NONDESTRUCTIVE EVALUATION, 2021, 40 (04)
  • [6] Data enhancement and multi-feature learning model for pest classification
    Zhao, Shulin
    Sun, Xiaoting
    Gai, Lingyun
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 45 (04) : 5409 - 5421
  • [7] A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction
    Xiao, Ling
    An, Ruofan
    Zhang, Xue
    PROCESSES, 2024, 12 (04)
  • [8] Localization and Mapping Based on Multi-feature and Multi-sensor Fusion
    Li, Danni
    Zhao, Yibing
    Wang, Weiqi
    Guo, Lie
    INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2024, 25 (06) : 1503 - 1515
  • [9] A Transfer Learning Method with Multi-feature Calibration for Building Identification
    Mao, Jiafa
    Yu, Linlin
    Yu, Hui
    Hu, Yahong
    Sheng, Weiguo
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [10] Epileptic Seizure Prediction Using Deep Neural Networks Via Transfer Learning and Multi-Feature Fusion
    Yu, Zuyi
    Albera, Laurent
    Jeannes, Regine Le Bouquin
    Kachenoura, Amar
    Karfoul, Ahmad
    Yang, Chunfeng
    Shu, Huazhong
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2022, 32 (07)