基于低秩表征学习的图像记忆性预测模型 下载: 573次
褚晶辉, 顾慧敏, 苏育挺. 基于低秩表征学习的图像记忆性预测模型[J]. 激光与光电子学进展, 2018, 55(7): 071002.
Chu Jinghui, Gu Huimin, Su Yuting. Image Memorability Prediction Model Based on Low-Rank Representation Learning[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071002.
[1] Isola P, Xiao J, Torralba A, et al. What makes an image memorable [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2011: 145-152.
[2] 陈长远, 韩军伟, 胡新韬, 等. 基于视觉显著熵与Object Bank特征的图像记忆性模型[J]. 计算机应用, 2013, 33(11): 3176-3178.
Chen C Y, Han J W, Hu X T, et al. Image memorability model based on visual saliency entropy and Object Bank feature[J]. Journal of Computer Applications, 2013, 33(11): 3176-3178.
[3] Isola P, Parikh D, Torralba A, et al. Understanding the intrinsic memorability of images[C]. International Conference on Neural Information Processing Systems, 2011: 2429-2437.
[4] Khosla A, Raju A S, Torralba A, et al. Understanding and predicting image memorability at a large scale[C]. IEEE International Conference on Computer Vision, 2015: 2390-2398.
[5] Peng H, Li K, Li B, et al. Predicting image memorability by multi-view adaptive regression[C]. International Conference on Multimedia, 2015: 1147-1150.
[6] Jing P, Su Y, Nie L, et al. Predicting image memorability through adaptive transfer learning from external sources[J]. IEEE Transactions on Multimedia, 2017, 19(5): 1050-1062.
[7] 薛志祥, 余旭初, 谭熊, 等. 局部超图拉普拉斯约束的高光谱影像低秩表示去噪方法[J]. 光学学报, 2017,37(5): 0510001.
[8] Ma L, Wang C, Xiao B, et al. Sparse representation for face recognition based on discriminative low-rank dictionary learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2586-2593.
[9] 翁嘉文, 谭穗妍. 自干涉非相干全息成像系统分辨率[J]. 中国激光, 2016, 43(6): 0609006.
[10] 刘帆, 刘鹏远, 李兵, 等. TensorFlow平台下的视频目标跟踪深度学习模型设计[J]. 激光与光电子学进展, 2017, 54(9): 091501.
[11] Li J, Chang H, Yang J. Learning discriminative low-rank representation for image classification[C]. International Joint Conference on Neural Networks, 2014: 313-318.
[12] Liu G C,Lin Z C,Yan S C,et al.Robust recovery of subspace structures by low-rank representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 171-184.
[13] 练秋生, 夏长城. 基于双树复数小波局部高斯模型的彩色图像压缩感知[J]. 激光与光电子学进展, 2011, 48(10): 101001.
[14] Lin Z, Liu R, Su Z. Linearized alternating direction method with adaptive penalty for low-rank representation[J]. Advances in Neural Information Processing Systems, 2011: 612-620.
[15] 侯榆青, 金明阳, 贺小伟, 等. 基于随机变量交替方向乘子法的荧光分子断层成像[J]. 光学学报, 2017, 37(7): 0717001.
[16] Xiao J, Hays J, Ehinger K A, et al. SUN database: Large-scale scene recognition from abbey to zoo[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2010: 3485-3492.
[17] Russell B C, Torralba A, Murphy K P, et al. LabelMe: A database and web-based tool for image annotation[J]. International Journal of Computer Vision, 2008, 77: 157-173.
[18] 崇伟, 沙奕卓, 行鸿彦, 等. 一种基于支持向量机回归的旋转遮光带日射表散射辐照度修正新算法[J]. 光学学报, 2012, 32(1): 0112001.
[19] Lu Y, Dhillon P S, Foster D, et al. Faster ridge regression via the subsampled randomized hadamard transform[C]. International Conference on Neural Information Processing Systems, 2013: 369-377.
[20] Hou C, Nie F, Yi D, et al. Efficient image classification via multiple rank regression[J]. IEEE Transactions on Image Processing, 2013, 22(1): 340-352.
[21] Yang Y, Song J, Huang Z, et al. Multi-feature fusion via hierarchical regression for multimedia analysis[J]. IEEE Transactions on Multimedia, 2013, 15(3): 572-581.
褚晶辉, 顾慧敏, 苏育挺. 基于低秩表征学习的图像记忆性预测模型[J]. 激光与光电子学进展, 2018, 55(7): 071002. Chu Jinghui, Gu Huimin, Su Yuting. Image Memorability Prediction Model Based on Low-Rank Representation Learning[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071002.