Deakin University
Browse

File(s) under permanent embargo

A New Improved Convolutional Neural Network Flower Image Recognition Model

conference contribution
posted on 2019-01-01, 00:00 authored by M Qin, Y Xi, F Jiang
© 2019 IEEE. In order to improve the accuracy of the flower image recognition, a convolutional neural network (A-LDCNN) model based on attention mechanism and LD-loss (Linear Discriminant Loss Function) is proposed. Unlike traditional CNN (Convolutional Neural Networks), A-LDCNN uses the VGG-16 network pre-trained by ImageNet to perform feature learning on preprocessed flower images. The attention feature is constructed by fusing the local features of the multiple intermediate convolution layers with the global features of the fully connected layer and using it as the final classification feature. LDA (Latent Dirichlet Allocation) is introduced into the model to construct a new loss function LD-loss, which participates in the training of CNN to minimize the feature distance in class and maximize the feature distance between classes, and to solve the problem of Inter-class similarity and intra-class difference in flower image classification. Classification experiments show that the accuracy of A-LDCNN is 87.6%, which is higher than other traditional networks and can realize the accurate recognition of flower images under natural conditions.

History

Pagination

3110-3117

Location

Xiamen, China

Start date

2019-12-06

End date

2019-12-09

ISBN-13

9781728124858

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

SSCI 2019 : Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence

Event

Computational Intelligence. Symposium (2019 : Xiamen, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC