Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1864-1869

Public environment image caption generation based on Se-ResNet-50 feature encoder

Tang Yu
He Zhiqin
Zhou Yuhui
Wu Qinmu
Wang Xiao
Electrical Engineering College, Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0490

Abstract

Aiming at the problem that the encoder-decoder structure in the traditional public environment image description model has insufficient feature extraction ability in the encoding process and the serious loss of context information in the decoding process, this paper proposed a public environment image caption model based on Se-ResNet-50 and M-LSTM. It added the SeNet module to the residual path of ResNet-50 to obtain the improved residual network to extract image features, and weighted each part of the feature to generate different attention feature maps. It input the fused text feature vector to the improved and long short-term memory network(M-LSTM) training with additional gating operations. After the model training, input the public environment image to get the natural sentence describing the image content. It evaluated the model on a variety of datasets. The experimental results show that the proposed model has improved by 3.2%, 2.1%, 1.7%, 1.7%, 1.3%, 8.2% on BLEU-1, BLEU-2, BLEU-3, BLEU-4, METER, CIDEr and other evaluation indicators respectively compared with the traditional model on MSCOCO datasets, which proves that the method has certain advantages in evaluation indicators and semantic diversity.

Foundation Support

贵州省科学技术基金资助项目(黔科合支撑［2021］一般264)
贵州省科学技术基金资助项目(黔科合支撑［2021］一般442)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0490
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Technology of Graphic & Image
Pages: 1864-1869
Serial Number: 1001-3695(2023)06-042-1864-06

Publish History

[2023-01-02] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

唐渔, 何志琴, 周宇辉, 等. 基于Se-ResNet50特征编码器的公共环境图像描述生成 [J]. 计算机应用研究, 2023, 40 (6): 1864-1869. (Tang Yu, He Zhiqin, Zhou Yuhui, et al. Public environment image caption generation based on Se-ResNet-50 feature encoder [J]. Application Research of Computers, 2023, 40 (6): 1864-1869. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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