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- Title
基于分层精简双线性注意力网络的鱼类识别.
- Authors
董绍江; 刘 伟; 蔡巍巍; 饶志荣
- Abstract
Due to the difficulty in collecting underwater fish images, the existing datasets are mainly extracted from videos. The collected fish images have problems such as complex background environment and low pixels, making the task of fine-grained fish images recognition more difficult. To solve the above problems, a network based on spatial domain attention mechanism and hierarchical compact bilinear feature fusion is proposed. The recognition network can be trained end-to-end, and consists of two parts: the first part is a background filtering network with a spatial transformation network (STN) as the attention mechanism; the second part is a vgg16 network as a feature extractor, for the difference in the response of the fine-grained features of fish images, which is based on the high-level convolution part, three groups of features are selected for network simplification and fusion of dimensionality reduction approximation, and finally the three groups of fused features are cascaded and sent to the soft-max classifier. The feature extraction network is initialized with the parameters trained on the ImageNet dataset and further fine-tuned using the fish dataset. Through comparison and verification on the F4K fish dataset, the results show that the proposed hierarchical compact bilinear attention network (STN-H-CBP) can reduce the feature dimension and reduce the amount of calculation at the same time. The performance is comparable to existing best practices.
- Subjects
FEATURE extraction; SPATIAL filters; PROBLEM solving; IMAGE recognition (Computer vision); BEST practices; PIXELS
- Publication
Journal of Computer Engineering & Applications, 2022, Vol 58, Issue 5, p186
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2009-0349