Memorable basis: Towards human-centralized sparse representation

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.