具有普适性的改进非负矩阵分解图像特征提取方法  

Image feature extraction method based on improved nonnegative matrix factorization with universality

作　　者：贾旭[1] 孙福明[1] 李豪杰[2] 曹玉东[1] 

机构地区：[1]辽宁工业大学电子与信息工程学院,辽宁锦州121001 [2]大连理工大学软件学院,辽宁大连116024

出　　处：《计算机应用》2018年第1期233-237,254,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61502216;61572244)~~

摘　　要：为提高图像特征提取的普适性,提出了一种基于改进非负矩阵分解(NMF)的图像特征提取方法。首先,考虑到提取的图像特征的实际意义,选用非负矩阵分解模型进行图像特征的降维处理;其次,为实现用较小数量系数来描述图像特征,将稀疏约束作为非负矩阵分解模型的正则项之一;然后,为使降维后优化得到的特征具有较好的类间区分性,将聚类属性作为非负矩阵分解的另一个正则项;最后,通过对模型的梯度下降优化求解,获得最优的特征基向量与图像特征向量。实验结果表明,针对3种图像数据库,所提的图像特征更有利于图像正确分类或识别,错误接受率(FAR)与错误拒绝率(FRR)分别可以降低到0.021与0.025。To improve the universality of image feature extraction, an image feature extraction method based on improved Nonnegative Matrix Factorization （NMF） was proposed. Firstly, considering the practical significance of extracted image features, NMF model was used to reduce the dimension of image feature vector. Secondly, in order to represent the image by a small number of coefficients, a sparse constraint was added to the NMF model as one of the regular terms. Then, to make the optimized feature have better inter-class differentiation, the clustering property constraint would be another regular term of the NMF model. Finally, through optimizing the model by using gradient descent method, the best feature basis vector and image feature vector could be acquired. The experimental results show that for three image databases, the acquired features extracted by the improved NMF model are more conducive to correct image classification or identification, and the False Accept Rate （FAR） and False Reject Rate （FRR） are reduced to 0. 021 and O. 025 respectively.

关 键 词：非负矩阵分解 特征提取 稀疏表示 梯度下降法 特征降维 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]