计算机工程与应用2018，Vol.54Issue(8)：178-182,254,6.DOI:10.3778/j.issn.1002-8331.1610-0018
基于RGB-D融合特征的图像分类
Image classification based on RGB-D fusion feature
摘要
Abstract
The classic image classification algorithms are mostly based on RGB or grayscale images,and the depth infor-mation of the object or scene has not been utilized effectively.To solve this problem,this paper proposes an image classifi-cation method based on RGB-D fusion feature.Firstly,the dense SIFT feature of color image is fused with the global Gist feature of the depth image to generate a combined vector.Secondly,the improved K-means algorithm is used to build the visual dictionary of the fusion feature,overcoming the dependence on the initial point selection of traditional K-means algorithm. Moreover, in the stage of image representation, the approximate LLC feature coding method is introduced to operate sparse coding on feature base and its corresponding visual dictionary.Finally,the linear SVM is used for image classification.The experimental results show that the proposed algorithm can effectively improve the classification accuracy.关键词
深度图像/dense尺度不变特征变化(SIFT)特征/Gist特征/K-means算法/局部约束线性编码(LLC)稀疏编码Key words
depth image/dense Scale Invariant Feature Transform(SIFT)feature/Gist feature/K-means/Locality-constrained Linear Coding(LLC)sparse coding分类
信息技术与安全科学引用本文复制引用
向程谕,王冬丽,周彦,李雅芳..基于RGB-D融合特征的图像分类[J].计算机工程与应用,2018,54(8):178-182,254,6.基金项目
国家自然科学基金(No.61100140,No.61104210,No.61175008). （No.61100140,No.61104210,No.61175008）
