基于投影熵特征的图像识别算法  

Image recognition algorithm based on projection entropy

作　　者：邵楠[1] 张科[1] 

机构地区：[1]西北工业大学航天学院,西安710072

出　　处：《计算机应用》2013年第10期2874-2877,共4页journal of Computer Applications

摘　　要：原始定义下的投影熵特征对于图像信息利用不够充分,而且对图像缩放变换不具有不变性,针对这两方面的不足,给出了扩展规范化投影熵特征的定义,并将规范化后图像的局部投影熵特征向量用于图像识别;在进行图像识别时,利用期望最大化(EM)算法得到训练集图像局部投影熵特征的混合高斯概率分布模型,求取目标图像的相应特征到各个混合高斯函数的Mahalanobis距离,根据距离判别法原理得到目标图像所属类别。实验采用哥伦比亚大学计算机视觉数据库中的图像对算法进行验证,结果表明该算法具有较好的识别效果和良好的并行运算特性。A method based on projection entropy for image recognition was introduced in this paper. Since original definition of projection entropy does not make full use of image information and is not scale invariant, a new definition was proposed. The Local Projection Entropy （LPE） of normalized image was used for image recognition. In the process of recognition, firstly, Gaussian Mixture Model （GMM） of training set images＇ LPE was obtained by Expectation Maximization （EM） algorithm. Then the Mahalanobis distance of target image＇ s LPE and GMM was calculated. The category of image was determined according to the distance discriminant law. Computer vision laboratory databases of Columbia university were used in the experiments, and the results show that the proposed algorithm is an effective approach for image recognition and has a proper structure for parallel computing.

关 键 词：图像识别 投影熵 混合高斯模型 最大期望算法 判别分析 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]