A fast fixed-point algorithm for independent component analysis
Neural Computation
Atomic Decomposition by Basis Pursuit
SIAM Review
What makes an image memorable?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Saliency Detection by Multitask Sparsity Pursuit
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Previous studies of sparse representation in multimedia research focus on developing reliable and efficient dictionary learning algorithms. Despite the sparse prior, how to integrate other related perceptual factors of human being into dictionary learning process was seldom studied. In this paper, we investigate the influence of image memorability for human-centralized sparse representation. Based on the results of a photo memory game, we are able to quantitatively characterize an image's memorability which allows us to train sparse bases from the most memorable images instead of randomly selected natural images. We believed that such kind of basis is more consistent with neural networks in human brain and hence can better predict where human looks. To test our hypothesis, we choose human eye-fixation prediction problem for quantitative evaluation. The experimental results demonstrate the superior performance of our Memorable Basis compared to traditional sparse basis trained from unselected images.