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- Title
Furniture Image Classification Based on Depthwise Group Over-Parameterized Convolution.
- Authors
Ye, Han; Zhu, Xiaodong; Liu, Chengyang; Yang, Linlin; Wang, Aili
- Abstract
In this paper, an improved VGG16 combined with depthwise group over-parameterized convolution (DGOVGG16) model is proposed to realize automatic furniture image classification. Firstly, depthwise over-parameterized convolution combined with group convolution is combined to construct depthwise group over-parameterized convolution, which is introduced to the VGG 16 model for reducing the number of parameters of the overall model while extracting more sufficient semantic features of furniture images. Then, this paper uses the ReLU activation function in the former part of the neural network to reduce the correlation between parameters and accelerate the weight update speed of the former part of the model. Meantime, the proposed model applies Leaky-ReLU activation function in the last layer to avoid the problem that some neurons do not update. Compared with the six furniture image classification methods based on MobileNetV2, AlexNet, ShuffleNetv2, GoogleNet, VGG 16 and GVGG16, the experimental results show the proposed DGOVGG16 with average accuracy (AA) of 95.51% has better classification performance.
- Subjects
FURNITURE; CLASSIFICATION; MATHEMATICAL convolutions
- Publication
Electronics (2079-9292), 2022, Vol 11, Issue 23, p3889
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics11233889