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
  • [41] Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network
    Huang, Mengmeng
    Jiang, Mingfeng
    Li, Yang
    He, Xiaoyu
    Wang, Zefeng
    Wu, Yongquan
    Ke, Wei
    Shengwu Yixue Gongchengxue Zazhi/Journal of Biomedical Engineering, 2025, 42 (01): : 49 - 56
  • [42] 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
  • [43] A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion
    Liu Y.
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [44] Smoke Detection Based on Multi-feature Fusion
    Wu Dongmei
    Wang Nana
    Yan Hongmei
    2012 5TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP), 2012, : 220 - 223
  • [45] Subjectivity Detection Based on Multi-feature Fusion
    Tian, Weixin
    Sun, Shuifa
    Wang, Anhui
    2012 WORLD AUTOMATION CONGRESS (WAC), 2012,
  • [46] Knowledge tracing based on multi-feature fusion
    Yongkang Xiao
    Rong Xiao
    Ning Huang
    Yixin Hu
    Huan Li
    Bo Sun
    Neural Computing and Applications, 2023, 35 : 1819 - 1833
  • [47] Generative multi-view and multi-feature learning for classification
    Li, Jinxing
    Zhang, Bob
    Lu, Guangming
    Zhang, David
    INFORMATION FUSION, 2019, 45 : 215 - 226
  • [48] Knowledge tracing based on multi-feature fusion
    Xiao, Yongkang
    Xiao, Rong
    Huang, Ning
    Hu, Yixin
    Li, Huan
    Sun, Bo
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (02): : 1819 - 1833
  • [49] Image retrieval based on multi-feature fusion
    Dong Wenfei
    Yu Shuchun
    Liu Songyu
    Zhang Zhiqiang
    Gu Wenbo
    2014 FOURTH INTERNATIONAL CONFERENCE ON INSTRUMENTATION AND MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC), 2014, : 240 - 243
  • [50] Multi-Feature Fusion for Enhancing Image Similarity Learning
    Lu, Jian
    Ma, Cheng-Xian
    Zhou, Yan-Ran
    Luo, Mao-Xin
    Zhang, Kai-Bing
    IEEE ACCESS, 2019, 7 : 167547 - 167556