计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2668-2672.DOI: 10.11772/j.issn.1001-9081.2014.09.2668
收稿日期:
2014-03-11
修回日期:
2014-05-09
出版日期:
2014-09-01
发布日期:
2014-09-30
通讯作者:
吕煊
作者简介:
基金资助:
国土资源部公益性项目
LYV Xuan1,LIU Yushu2,DING Hongfu1,LI Aidi1
Received:
2014-03-11
Revised:
2014-05-09
Online:
2014-09-01
Published:
2014-09-30
Contact:
LYV Xuan
摘要:
字典模型(BOW)是一种经典的图像描述方法,模型中特征字典的构造方法至关重要。针对特征字典构造问题,提出了一种类别约束下的低秩优化特征字典构造方法LRC-DT,通过低秩优化的方法使训练出来的特征字典在描述同类图像时表示系数矩阵的秩相对较低,从而将类别信息引入到字典学习中,提高字典对图像描述的可分辨性。在标准公测库Caltech-101和Caltech-256上的实验结果表明:将SPM、稀疏编码下的SPM(ScSPM)、局部线性编码(LLC)和线性核函数的SPM(LSPM)编码方法中的特征字典替换为加入低秩约束(LRC)的特征字典后,随着训练样本数目增多,字典模型的分类准确率与未引入低秩约束的方法相比有所提高。
中图分类号:
吕煊 刘玉淑 丁洪富 李爱迪. 类别约束下的低秩优化特征字典构造方法[J]. 计算机应用, 2014, 34(9): 2668-2672.
LYV Xuan LIU Yushu DING Hongfu LI Aidi. Low-rank optimization characteristic dictionary training approach with category constraint[J]. Journal of Computer Applications, 2014, 34(9): 2668-2672.
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