摘要
针对场景分类问题,本文提出一种基于图像局部边缘区域的EILBP(Edge Improved Local Binary Pattern)视觉特征描述结合PLSA模型场景分类方法。EILBP视觉特征通过利用局部边缘区域的梯度、方向分布与特征的局部空间分布等信息对图像进行充分合理地描述。首先对场景图像边缘轮廓稠密采样,得到以稠密采样点为中心的图像局部边缘区域并提取区域的EILBP特征作为视觉词汇,对视觉词汇聚类形成视觉词汇表(码本);然后,用词袋(BOW,Bag-Of-Words)模型描述图像;最后,利用PLSA模型对图像的词袋模型进行潜在语义挖掘并用判定式KNN分类器进行场景分类,得到测试图像集合的混淆矩阵。在多类场景图像上的实验表明,本文所用的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图像分类精度较高。
A novel approach based on the Edge Improved Local Binary Pattern（EILBP） visual feature and PLSA model for scene classification was presented.Moreover,the EILBP features could not only capture the distribution information of the local edge gradient and direction,but also obtain the local structures information for describing image.At first,EILBP features were extracted from edge regions as visual words,and then these visual words were formed by clustering method.After that,the Bag-Of-Words（BOF） model was used to represent the image contents.At last,the potential semantics was excavated by PLSA model and the confusion matrix was obtained by KNN classifier.Experiment results show that this method achieves higher accuracies,especially performs well in the images with much edge contours and also it needn＇t require experts to annotate the scene content in advance.
出处
《光电工程》
CAS
CSCD
北大核心
2010年第11期128-134,共7页
Opto-Electronic Engineering
基金
国家自然科学基金(61071199)
河北省自然科学基金(F2010001297)
河北省自然科学基金(F2008000891)
中国博士后自然科学基金(20080440124)
第二批中国博士后基金特别资助(200902356)
关键词
场景分类
边缘局部二进制模式
视觉单词
PLSA模型
scene classification
Edge Improved Local Binary Pattern（EILBP）
visual words
PLSA model
