计算机应用与软件2017，Vol.34Issue(7)：143-148,6.DOI:10.3969/j.issn.1000-386x.2017.07.027
基于平均区域划分的Laplacian稀疏编码的图像分类
IMAGE CLASSIFICATION BASED ON AVERAGE REGION PARTITIONING AND LAPLACIAN SPARSE CODING
摘要
Abstract
For the sparse coding method, the coding process is unstable and the pyramid matching method can not make the fusion feature very sparse, an image classification method based on Laplacian sparse coding with average region partition is proposed.Firstly, local invariant feature transform (SIFT) feature extraction was applied to the original image.Then, Laplacian regularization was added to the sparse coding method to encode the local features so that the similar features have similar code words and the feature vectors were fused by average region partition and max pooling.Finally, multi-class SVM classifier was used to classify the images.Experimental results on several standard image datasets show that the algorithm has higher classification accuracy.关键词
稀疏编码/Laplacian正则化/平均区域划分/最大值融合Key words
Sparse coding/ Laplacian regularization/ Average region division/ Maximum fusion分类
信息技术与安全科学引用本文复制引用
史莹,万源,陈晓丽..基于平均区域划分的Laplacian稀疏编码的图像分类[J].计算机应用与软件,2017,34(7):143-148,6.基金项目
国家自然科学基金项目(81271513,91324201). （81271513,91324201）
