A Framework of Joint Low-rank and Sparse Regression for Image Memorability Prediction
IEEE Transactions on Circuits and Systems for Video Technology(2019)
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.
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关键词
Sparse matrices,Visualization,Robustness,Matrix decomposition,Task analysis,Approximation algorithms,Heuristic algorithms
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