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Tourism image classification based on convolutional neural network SqueezeNet--Taking Slender West Lake as an
Lantao Xu1, Xuegang Chen1, Xinlu Yang2
1School of Geographical Science and Tourism, Xinjiang Normal University, Urumqi, Xinjiang, China.
Plos One
|January 29, 2024
Summary
This study enhances tourism image classification using a lightweight convolutional neural network (CNN). The improved SqueezeNet model achieves 85.75% accuracy with a small file size, aiding tourism resource development.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Tourism Studies
Background:
- Human vision alone is insufficient for in-depth analysis of tourist perceptions of tourism resources.
- Convolutional Neural Networks (CNNs) offer new possibilities for automated tourism image classification.
Purpose of the Study:
- To improve tourism image classification accuracy and efficiency.
- To develop a lightweight model for classifying tourism images.
Main Methods:
- The study utilized a dataset of 3740 Slender West Lake tourism images.
- An existing lightweight CNN model, SqueezeNet, was selected and modified.
- The improved model was trained and validated on the tourism image dataset.
Main Results:
- The enhanced SqueezeNet model achieved a validation accuracy of 85.75%.
- The final model size was reduced to 2.64 MB.
- The results demonstrate high accuracy classification with reduced parameters.
Conclusions:
- The improved SqueezeNet model offers an effective solution for tourism image classification.
- This research provides a scientific reference for tourism image studies and resource planning.
- The study indicates a new direction for the development and planning of tourism resources.
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