Memorability of Image Regions
While long term human visual memory can store a remarkable amount of visual information, it tends to degrade over time. Recent works have shown that image memorability is an intrinsic property of an image that can be reliably estimated using state-of-the-art image features and machine learning algorithms. However, the class of features and image information that is forgotten has not been explored yet. In this work, we propose a probabilistic framework that models how and which local regions from an image may be forgotten using a data-driven approach that combines local and global images features. The model automatically discovers memorability maps of individual images without any human annotation. We incorporate multiple image region attributes in our algorithm, leading to improved memorability prediction of images as compared to previous works.
Visual Memory Game
Image Memorability and Visual Inception, Aditya Khosla, Jianxiong Xiao, Phillip Isola, Antonio Torralba, Aude Oliva. In SIGGRAPH Asia, 2012 (invited paper, technique briefs section)
Understanding the Intrinsic Memorability of Images, Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva. In Advances in Neural Information Processing Systems (NIPS), 2011
What makes an image memorable?, Phillip Isola, Jianxiong Xiao, Antonio Torralba, Aude Oliva. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011
We thank Phillip Isola and the reviewers for helpful discussions. This work is funded by NSF grant (1016862) to A.O, Google research awards to A.O and A.T, ONR MURI N000141010933 and NSF Career Award (0747120) to A.T. J.X. is supported by Google U.S./Canada Ph.D. Fellowship in Computer Vision.
For comments and questions, please contact Aditya Khosla.