卷积神经网络和隐藏的马尔科夫链的新混合模型用于图像分类
Soumia Goumiri1,2, Dalila Benboudjema1, Wojciech Pieczynski3
1Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole nationale Supérieure d'Informatique (ESI), BP, 68M Oued-Smar, 16270 Alger, Algeria.
Neural computing & applications
|June 26, 2023
概括
这项研究引入了一种新的混合模型,将卷积神经网络 (CNN) 与隐藏的马尔科夫链 (HMC) 结合起来,以改进图像分类. 与传统的CNN和其他混合方法相比,CNN-HMC模型显著提高了准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 在图像识别方面非常出色.
- 隐藏的马尔科夫链 (HMCs) 是建立的图像处理的概率模型.
- 现有的CNN模型可以进一步优化,以提高图像分类性能.
研究的目的:
- 推出一种新的混合模型,CNN-HMC,将CNN和HMC集成在一起,以获得更优质的图像分类.
- 为了在统一的框架内利用CNN来提取特征和HMC进行分类.
- 为了提高基于CNN的图像分类任务的准确性和性能.
主要方法:
- 开发了一个CNN-HMC模型,利用CNN用于特征提取和HMC用于分类.
- 采用卷积和聚合层来提取特征地图和Peano扫描来生成HMC.
- 使用预期最大化 (EM) 算法对HMC参数估计和贝叶斯最大后方模式 (MPM) 进行无监督分类.
主要成果:
- 据报道,CNN-HMC模型在4Conv和Mini AlexNet等经典CNN上表现出了卓越的性能.
- 观察到显著的准确性改进,CNN-HMC实现了81.63%92.5%的准确性,而基线CNN的准确性为71%.
- 在五个数据集和四个指标 (回忆,精度,F1分数,准确性) 上进行的比较实验证实了CNN-HMC方法的有效性.
结论:
- 拟议的CNN-HMC混合模型在图像分类方面取得了重大进展.
- 这种方法通过整合基于HMC的分类,有效地提高了CNN的性能.
- CNN-HMC模型为复杂的图像分类挑战提供了强大而准确的解决方案.
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