Image sentiment analysis method based on multi-level feature fusion

Tao Sun; Shuang Wang; Shenjie Zhong

doi:10.1117/12.2640791

13 October 2022 Image sentiment analysis method based on multi-level feature fusion

Tao Sun, Shuang Wang, Shenjie Zhong

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871A (2022) https://doi.org/10.1117/12.2640791
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

With the development of social platforms, people are more likely to use pictures to share their sentiments and opinions. With the development of deep learning, the current mainstream image sentiment analysis research mainly focuses on the use of high-level semantic sentiment features of images, and less on the use of low-level sentiment features such as colors, lines and so on. Aiming at the above problems, this paper proposes an image sentiment analysis model based on multi-level feature fusion (MLFF). Firstly, the convolution neural network is used to learn different levels of image features; Then the multi-level features are input into the bidirectional sequential neural network, and attention is introduced to fuse to generate multi-level fusion features; Finally, the multi-level fusion features are input to the full connection layer for classification. The experimental results show that the fusion model proposed in this paper can make effective use of multi-level image features and effectively improve the performance of image sentiment analysis.

Citation Download Citation

Tao Sun, Shuang Wang, and Shenjie Zhong "Image sentiment analysis method based on multi-level feature fusion", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871A (13 October 2022); https://doi.org/10.1117/12.2640791

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KEYWORDS

Image analysis

Image fusion

Neural networks

Analytical research

Feature extraction

Convolution

Convolutional neural networks

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