Nonnegative correlation coding for image classification  

作　　者：Zhen DONG Wei LIANG Yuwei WU Mingtao PEI Yunde JIA 

机构地区：[1]Beijing Laboratory of Intelligent Information Technology, School of Computer Science,Beijing Institute of Technology

出　　处：《Science China(Information Sciences)》2016年第1期103-116,共14页中国科学（信息科学）（英文版）

基　　金：supported in part by National Basic Research Program of China(973)(Grant No.2012CB720000);National Natural Science Foundation of China(NSFC)(Grant No.61203291);Specialized Research Fund for the Doctoral Program of Chinese Higher Education(Grant No.20121101110035)

摘　　要：Feature coding is one of the most important procedures in the bag-of-features model for image classification. In this paper, we propose a novel feature coding method called nonnegative correlation coding. In order to obtain a discriminative image representation, our method employs two correlations： the correlation between features and visual words, and the correlation between the obtained codes. The first correlation reflects the locality of codes, i.e., the visual words close to the local feature are activated more easily than the ones distant. The second correlation characterizes the similarity of codes, and it means that similar local features are likely to have similar codes. Both correlations are modeled under the nonnegative constraint. Based on the Nesterov＇s gradient projection algorithm, we develop an effective numerical solver to optimize the nonnegative correlation coding problem with guaranteed quadratic convergence. Comprehensive experimental results on publicly available datasets demonstrate the effectiveness of our method.Feature coding is one of the most important procedures in the bag-of-features model for image classification. In this paper, we propose a novel feature coding method called nonnegative correlation coding. In order to obtain a discriminative image representation, our method employs two correlations： the correlation between features and visual words, and the correlation between the obtained codes. The first correlation reflects the locality of codes, i.e., the visual words close to the local feature are activated more easily than the ones distant. The second correlation characterizes the similarity of codes, and it means that similar local features are likely to have similar codes. Both correlations are modeled under the nonnegative constraint. Based on the Nesterov＇s gradient projection algorithm, we develop an effective numerical solver to optimize the nonnegative correlation coding problem with guaranteed quadratic convergence. Comprehensive experimental results on publicly available datasets demonstrate the effectiveness of our method.

关 键 词：image classification correlation coding NONNEGATIVITY LOCALITY SIMILARITY 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]