基于EICS-LBP与统计边缘主色对的场景分类算法

Scene classification algorithm based on EICS-LBP and edge domain color pairs visual descriptors

针对场景分类问题,提出一种基于图像局部边缘区域的边缘改进中心对称二值模式(edge improvedcenter symmetric local binary pattern,EICS-LBP)与统计边缘主色对特征结合扩展潜在语义分析(probabilistic latent semantic analysis,PLSA)模型的场景分类算法。该方法首先提取图像局部边缘稠密采样区域的EICS-LBP与统计边缘主色对特征;然后对两类特征分别聚类形成视觉词汇表,进而用词袋模型描述图像;之后利用扩展PLSA模型对图像词袋模型进行潜在语义挖掘;最后利用K最近领域(K-nearest neighbors,KNN)分类器进行场景分类,得到测试图像集的混淆矩阵。多类场景图像的实验表明,该方法不需要对场景内容进行人工标注,具有较高的分类准确率,且对具有边缘轮廓的彩色图像分类精度较高。

A novel approach based on the edge improved center symmetric local binary pattern（EICS-LBP） and the statistical domain color pairs of edge as visual features combined with the extended probabilistic latent semantic analysis（PLSA） model for scene classification is presented.First,the features are extracted from edge dense sampling regions as visual words,and then these visual words are formed by clustering respectively.After that,the bag-of-words model is used to represent the image.And then,the potential semantic is excavated by the extended PLSA model.Finally,the confusion matrix is obtained by K-nearest neighbors（KNN） classifier.Experiment results show that this method achieves higher accuracies,especially performs well in the color images with much edge contours and also it does not require experts to annotate the scene content in advance.