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Deep Neural Networks for Image-Based Dietary Assessment
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生物启发的被发现的海优化器具有基于深度卷积神经网络的自动化食物图像分类
Hany Mahgoub1, Ghadah Aldehim2, Nabil Sharaf Almalki3
1Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Muhayil 61321, Saudi Arabia.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
这项研究引入了一种新的方法,用于使用深度卷积神经网络 (DCNN) 进行自动化食物图像分类,该神经网络通过生物启发的斑点羊优化器 (SHO) 进行了优化. 这种方法提高了从图像中识别各种食品的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 食品图像分类对于饮食监测和餐厅推等应用至关重要.
- 深度学习 (DL) 和卷积神经网络 (CNN) 已经显著提升了自动图像分类.
- 现有的方法需要优化,以提高复杂的食品图像数据集的准确性.
研究的目的:
- 开发一个先进的自动化食品图像分类系统.
- 提高从图像识别食品的准确性和效率.
- 引入一种新的混合方法,将DL与生物灵感优化相结合.
主要方法:
- 一个深度卷积神经网络 (DCNN) 使用Xception模型进行特征提取.
- 集成发现的海优化器 (SHO) 算法用于DCNN的超参数调整.
- 使用极端学习机器 (ELM) 模型进行最终的食品图像分类.
主要成果:
- 拟议的SHODCNN-FIC方法在食品图像分类方面表现优异,与其他DL模型相比.
- 实验结果验证了SHO算法在优化DCNN超参数方面的有效性.
- 实现了各种食品的准确分类,展示了该模型的稳定性.
结论:
- SHODCNN-FIC方法为自动化食品图像分类提供了一个非常有效的解决方案.
- DCNNs,SHO和ELM之间的协同作用显著提高了识别准确性.
- 这种方法具有各种应用的潜力,需要从视觉数据中精确识别食品.
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