A deep learning image classification method and its application introducing hybrid attention mechanism

Shaowei Pan; Jinyun Han; Zhi Guo; Jiaqing Zhang; Zebin Ju

doi:10.1117/12.3033561

13 June 2024 A deep learning image classification method and its application introducing hybrid attention mechanism

Shaowei Pan, Jinyun Han, Zhi Guo, Jiaqing Zhang, Zebin Ju

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800R (2024) https://doi.org/10.1117/12.3033561
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

We introduce a deep learning methodology that combines the Inception-ResNet-V2 architecture with a hybrid attention mechanism. This fusion enables the extraction of crucial information from both channel and spatial dimensions within feature maps, giving rise to the ECA-SA-Inception-ResNet model. To assess the performance of our model, we conducted experiments on a petrographic thin section image dataset utilizing Grad-CAM visualization techniques. The results demonstrate the model's proficiency in extracting essential features from petrographic thin section images, thereby resulting in substantial enhancements in image classification accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaowei Pan, Jinyun Han, Zhi Guo, Jiaqing Zhang, and Zebin Ju "A deep learning image classification method and its application introducing hybrid attention mechanism", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800R (13 June 2024); https://doi.org/10.1117/12.3033561

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KEYWORDS

Image classification

Feature extraction

Deep learning

Visualization

Performance modeling

Visual process modeling

Image enhancement

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