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.
@inproceedings{NIPS12_Khosla,
author = "Aditya Khosla and Jianxiong Xiao and Antonio Torralba and Aude Oliva",
title = "Memorability of Image Regions",
booktitle = "Advances in Neural Information Processing Systems (NIPS)",
year = "2012",
month = "December",
address = "Lake Tahoe, USA"
}
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.