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基于ECA-Net与多尺度结合的细粒度图像分类方法.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . Nov2021, Vol. 38 Issue 11, p3484-3488. 5p. - Publication Year :
- 2021
-
Abstract
- Aiming at the problem of fine-grained visual categorization, this paper proposed an effective algorithm to achieve end-to-end fine-grained visual categorization. The ECA module in ECA-Net was a channel attention mechanism with significant performance advantages. It model-fused with the classic network ResNet-50 to form the ResEca. Then, it used the object-level image positioning module and the part-level image generation module to generate object-level and part-level images. Those images combined with original images could be as the input of the new constructed network Tb-ResEca-Net. This paper trained the 95. 1% and 95. 3% respectively on the test set of the corresponding dataset. The experimental results show that this method has higher classification accuracy and stronger robustness compares with other traditional fine-grained classification methods, which is an effective fine-grained image classification method. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DEEP learning
*CLASSIFICATION
*ALGORITHMS
*MACHINE learning
Subjects
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 38
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 155349338
- Full Text :
- https://doi.org/10.19734/j.issn.1001-3695.2021.02.0074