We propose a novel LBCNN model with AM Softmax based on bilinear CNN (BCNN) and AM Softmax loss function, which can better fit fine-grained birds recognition tasks. There are mainly two contributions. Firstly, in order to reduce the model size and recognition time, we design a lightweight BCNN model to reduce the parameters. We replace original VGG16 backbone with MobileNet structure which decomposes the convolution operation into two smaller operations: depthwise revolution and pointwise revolution. Secondly, to make up for the decrease in accuracy, we introduce the Additive Margin Softmax (AM Softmax) loss function to enhance the discrimination ability. By comprehensive discussion of the influence of different parameter settings and different loss functions, we test the proposed lightweight BCNN on the bird dataset CUB-200-2011. Experimental results demonstrate that the proposed model can achieve comparable results with much fewer parameters.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.