摘要
为了不破坏原始数据固有的高阶结构和数据之间的相关性,减少光照对图像特征的影响,并优化多尺度特征的权重,提出了基于加权多尺度张量子空间的图像特征提取方法。采用多尺度小波变换表征图像各个部位特征,使用不确定度权衡每个尺度对图像分类的作用,并组建成多尺度张量子空间,结合多线性主成分分析与线性判别分析算法,降低了图像在处理过程中的成本,保存了高维数据固有结构和相关性,完成对图像特征提取。使用CAS-PEAL-R1东方人脸库进行评测,实验结果表明,该图像特征提取算法用于图像识别过程中具有较好的效果,具有一定的可行性。
In order to keep the inherent higher order structure and correlation in the original data, reduce the influence of illumination in image recognition, and optimize the weight of the multi-scale feature, the method of image feature extracting based on weighted multi-scale tensor subspace is proposed to solve the problems. Firstly, multi-scale transform is used to characterize each place feature of the image, and un- certainty weighed is adopted on the role of each scale feature for image classification. And then a multiscale tensor space is built using multiple linear principal component analysis and linear discriminant analy- sis algorithm to reduce the cost of processing, preserving the inherent structure and correlation of high- dimensional data. Finally, the extraction of the image features is completed. CAS-PEAL-R1 oriental face database is chosen for evaluation. The experimental results show that the algorithm performs better than some recent algorithms for image recognition with practical feasibility.
出处
《数据采集与处理》
CSCD
北大核心
2016年第4期791-798,共8页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61462042
614620145)资助项目
关键词
图像特征
多尺度变换
张量子空间
多线性主成分分析
不确定度
image feature
multi-scale transforrn
tensor subspace
multiple linear principle component analysis
uncertainty