南京理工大学学报（自然科学版）2016，Vol.40Issue(1)：61-66,6.DOI:10.14177/j.cnki.32-1397n.2016.40.01.010
用于图像识别的稀疏高斯编码
Sparse Gaussian coding for image recognition
摘要
Abstract
In order to solve the malconformation of clustering in the feature learning, the paper presents a sparse Gaussian coding based feature learning algorithm. It can be trained only through K-means clustering. In the encoding process it takes data's distribution into consideration. Given that the K-means clustering often results in unequal clusters,we also propose a feature selection method that can be used for denoising and dimension reduction. This model achieves high accuracy, and saves training time a lot. In this paper,we have designed a contrast experiment on the face database AR and the object database Caltech101 . The experimental results show that the algorithm is effective and robust.关键词
图像识别/深度学习/特征表示/稀疏高斯编码/特征学习/K-means聚类Key words
image recognition/deep learning/feature representation/sparse Gaussian coding/feature learning/K-means clustering分类
信息技术与安全科学引用本文复制引用
张少辉,王迤冉..用于图像识别的稀疏高斯编码[J].南京理工大学学报（自然科学版）,2016,40(1):61-66,6.基金项目
河南省科技厅软科学研究计划项目(142400411213,142400411133) （142400411213,142400411133）
河南省高等学校重点科研项目(15A520118) （15A520118）
河南省科技发展计划项目(NO. 152102310381) （NO. 152102310381）
