基于Gabor特征和字典学习的高斯混合稀疏表示图像识别  

Gaussian Mixture Sparse Representation for Image Recognition Based on Gabor Features and Dictionary Learning

作　　者：詹曙[1] 王俊[1] 杨福猛[2] 方琪[1] 

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009 [2]三江学院电子信息工程学院,江苏南京210012

出　　处：《电子学报》2015年第3期523-528,共6页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61371156);安徽省科技攻关计划(No.1401B042019)

摘　　要：为了克服图像识别中光照,姿态等变化带来的识别困难,同时提高稀疏表示图像识别的鲁棒性,本文提出了一种基于Gabor特征和字典学习的高斯混合稀疏表示图像识别算法.高斯混合稀疏表示是基于最大似然估计准则,将稀疏保真度表示为余项的最大似然函数,最终识别问题转化为求解加权范数的优化逼近问题.本文算法首先提取图像的Gabor特征;然后对Gabor特征集进行字典学习,由于在学习过程中引入了Fisher准则作为约束,学习得到具有类别标签的新字典;最后使用高斯混合稀疏表示识别方法进行分类识别.在3个公开数据库(人脸数据库AR库和FERET库以及USPS手写数字库)上的实验结果验证了该算法的有效性和鲁棒性.To overcome the problems of the illumination and pose variations in image recognition, the algorithm of Gaussian mixture sparse representation for image recognition based on dictionary learning and Gabor features is proposed. Based on the maxi- mum likelihood estimation principle, a mixture Gaussian sparse coding model is proposed to express the discriminating items to the maximum likelihood function of residuals,so the problem of identification is converted to the optimal weighted norm approximation problem. This approach extracts the Gabor features of the images by the Gabor filter, and then uses the Gabor features to learn a new dictionary.As the Fisher criterion is added in the learning process as a constraint, a new dictionary with category labels can be ob- tained. Finally, the method of Gaussian mixture sparse representation is used for classification and identification. The experimental results in three public databases demonstrate that the algorithm proposed is effective and robust.

关 键 词：GABOR特征 稀疏表示 fisher字典学习 最大似然估计 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]