摘要
针对静态手势图像的分类识别问题,提出了一种将无监督的特征学习和有监督的分类识别相结合的静态手势图像识别方法,通过无监督的稀疏自编码神经网络训练图像小块提取手势图像的边缘特征,并将此边缘特征作为训练分类器的输入,最后提出对训练好的分类器的参数进行调优从而提高准确率.
In order to solve static gesture image classification problems,the paper presents a unsupervised learning process combining the static supervised classification.According to the unsupervised sparse auto-encoder neural network,the image patches were trained to extract the edge feature of the image,and these edge features are the input of a classifier,and finally.And the classifier parameter was used to improve the classification accuracy.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第1期100-103,共4页
Microelectronics & Computer
