计算机应用与软件2016，Vol.33Issue(6)：215-219,5.DOI:10.3969/j.issn.1000-386x.2016.06.052
基于PCA-SIFT特征与贝叶斯决策的图像分类算法
IMAGE CLASSIFICATION ALGORITHM BASED ON PCA-SIFT FEATURES AND BAYESIAN DECISION
涂秋洁 1王晅1
作者信息
- 1. 陕西师范大学物理学与信息技术学院 陕西 西安710119
- 折叠
摘要
Abstract
In order to cope with the problems that existing SIFT-based image classification algorithms require a large amount of storage space and are sensitive to image backgrounds,this paper presents a novel image classification algorithm which is based on PCA-SIFT features and Bayesian decision.The algorithm first applies the principal component analysis (PCA)to reduce the dimensionality of SIFT from 128 to 36,in training process,it makes regional matching on PCA-SIFT descriptors of the training sample images.In order to improve its robustness on background image interference,we selected the stable PCA-SIFT descriptors in object images based on their matching rates,and then used the maximum likelihood estimation to estimate the probability distribution parameters.Finally we used Bayesian decision theory to implement the image classification.Simulation experiment showed that this algorithm has higher classification accuracy compared with existing SIFT-based image classification methods.It also has minimum storage space requirement and higher computation efficiency.关键词
PCASIFT/聚类/贝叶斯决策/图像分类Key words
PCA-SIFT/Clustering/Bayesian decision/Image classification分类
信息技术与安全科学引用本文复制引用
涂秋洁,王晅..基于PCA-SIFT特征与贝叶斯决策的图像分类算法[J].计算机应用与软件,2016,33(6):215-219,5.
