Fine-grained birds recognition based on lightweight bilinear CNN with Additive Margin Softmax

Yuchen Sun; Bingchen Shen; Ziqiao Jin; Zhaoying Liu

doi:10.1117/12.2644253

12 October 2022 Fine-grained birds recognition based on lightweight bilinear CNN with Additive Margin Softmax

Yuchen Sun, Bingchen Shen, Ziqiao Jin, Zhaoying Liu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420T (2022) https://doi.org/10.1117/12.2644253
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

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.

Citation Download Citation

Yuchen Sun, Bingchen Shen, Ziqiao Jin, and Zhaoying Liu "Fine-grained birds recognition based on lightweight bilinear CNN with Additive Margin Softmax", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420T (12 October 2022); https://doi.org/10.1117/12.2644253

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KEYWORDS

Convolution

Visual process modeling

Performance modeling

Data modeling

Neural networks

Image classification

Feature extraction

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