类别约束下的低秩优化特征字典构造方法

doi:10.11772/j.issn.1001-9081.2014.09.2668

计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2668-2672.DOI: 10.11772/j.issn.1001-9081.2014.09.2668

类别约束下的低秩优化特征字典构造方法

吕煊¹,刘玉淑²,丁洪富¹,李爱迪¹

1. 重庆市国土资源和房屋勘测规划院，重庆 400020；
2. 齐鲁工业大学电气与自动化学院，济南 250353

收稿日期:2014-03-11 修回日期:2014-05-09 发布日期:2014-09-30 出版日期:2014-09-01
通讯作者: 吕煊
作者简介:
吕煊(1982-),男,山东淄博人,工程师,博士,主要研究方向:图像分类、数据挖掘;
刘玉淑(1982-),女,山东淄博人,讲师,博士,主要研究方向:数字图像处理、模式识别;
丁洪富(1974-),男,重庆人,正高级工程师,主要研究方向:地理信息；
李爱迪(1979-)，男，四川人，高级工程师，主要研究方向：地理信息系统。
基金资助:
国土资源部公益性项目

Low-rank optimization characteristic dictionary training approach with category constraint

LYV Xuan¹,LIU Yushu²,DING Hongfu¹,LI Aidi¹

1. Chongqing Land Resources Housing Surveying and Planning Institute, Chongqing 400020, China
2. School of Electrical Engineering and Automation, Qilu University of Technology, Jinan Shandong 250353, China

Received:2014-03-11 Revised:2014-05-09 Online:2014-09-30 Published:2014-09-01
Contact: LYV Xuan

摘要/Abstract

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。

Abstract:

Bag Of Words (BOW) is a classical approach of image description, and the method of constructing the characteristic dictionary in this model is very important. A category constrained low-rank optimization characteristic dictionary training approach named LRC-DT was proposed for the characteristic dictionary construction. Through the low-rank optimization, the rank of the coefficient matrix constructed by same category images was minimized. Then the classification information was introduced into the characteristic dictionary learning to improve the identifiability of characteristic dictionary for image description. Some experiments were conducted on two standard image databases including Caltech-101 and Caltech-256, and the characteristic dictionary of SPM (Spatial Pyramid Matching), ScSPM (Sparse codes SPM), LLC (Locality-constrained Linear Coding) and LSPM (Linear SPM) were replaced by constrained low-rank optimization characteristic dictionary. The experimental results show that the proposed method can consistently offer better performance than not employing the category constrained low-rank optimization, its classification accuracy is improved with the increase of the training sample number.

中图分类号:

TP391

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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类别约束下的低秩优化特征字典构造方法

Low-rank optimization characteristic dictionary training approach with category constraint

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