A Framework of Joint Low-Rank and Sparse Regression for Image Memorability Prediction

被引:32
作者
Jing, Peiguang [1 ]
Su, Yuting [1 ]
Nie, Liqiang [2 ]
Gu, Huimin [1 ]
Liu, Jing [1 ]
Wang, Meng [3 ]
机构
[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
[2] Shandong Univ, Sch Comp Sci & Technol, Jinan 250000, Shandong, Peoples R China
[3] Hefei Univ Technol, Sch Comp & Informat Sci, Hefei 230009, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
Image memorability prediction; low-rank; sparse regression; subspace learning; ROBUST FACE RECOGNITION; THRESHOLDING ALGORITHM; REPRESENTATION;
D O I
10.1109/TCSVT.2018.2832095
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Image memorability is to measure the degree to which an image is remembered. Generally image memorability prediction involves two steps: feature representation and prediction. Most previous work just focused on addressing the first step by investigating the factors of making an image memorable. They not only lack the use of a learning mechanism in feature representation, but also often neglect the second step. In this paper, we first propose a joint low-rank and sparse regression (JLRSR) framework to address this problem. JLRSR aims to jointly learn: 1) a low-rank projection matrix that enables us to decompose the original data into a component part and an error part and 2) a sparse regression coefficient vector for image memorability prediction. The projection matrix and the regression coefficients are bound by a sparse constraint to make our approach invariant to training samples. Moreover, a graph regularizer is constructed to improve the generalization performance and prevent overfitting. We then extend JLRSR to a multi-view version called Mv-JLRSR by imposing the block-wise constraint to ensure the group effect and the view correlation constraint to eliminate the heterogeneity among views. Experiment results validate the effectiveness of our proposed approaches.
引用
收藏
页码:1296 / 1309
页数:14
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