摘要
针对目前光学图像识别准确率不高的问题，重点研究了基于小波空间特征谱熵的图像特征提取方法。该方法利用小波变换前后能量不变的原理，构造小波能量模式矩阵，对该矩阵进行奇异值分解，并求取奇异值的特征谱熵作为图像的特征。同时，还结合反向传播神经网络来进行图像识别。实验结果表明，所提出的图像特征提取方法能够获得较高的图像正确识别率，证明了该方法的有效性。
To solve the problem that present recognition accuracy of optical image is not high, the method of image feature extraction based on wavelet space feature spectrum entropy is studied deeply. According to the principle of energy is equal before and after the wavelet transform, the matrix of wavelet energy mode is constructed, and the singular value decomposition is done to the matrix, then the feature spectrum entropy of singular value is as the image feature. At the same time, the back propagation neural network is also used for image recognition. The experimental results show that the proposed image feature extraction method can obtain higher accuracy of image recognition, and the effectiveness of the method is also proved.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2015年第A01期118-123,共6页
Acta Optica Sinica
基金
国家自然科学基金（61304124,61205121,61306090）、浙江省自然科学基金（LY13F010009,LY15F050012）、国家博士后基金（2012M311386）、浙江工业大学自然科学基金重点项目（2013XZ003）
关键词
图像处理
光学图像
特征提取
小波变换
特征谱熵
image processing
optical image
feature extraction
wavelet transform
feature spectrum entropy
