Name
Abstract | ||
|---|---|---|
When glancing at a magazine, or browsing the Internet, we are continuously being exposed to photographs. Despite of this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stitch to our minds, and other are forgotten. In this paper we focus on the problem of predicting how memorable an image will be. We show that memorability is a stable property of an image that is shared across different viewers. We introduce a database for which we have measured the probability that each picture will be remembered after a single view. We analyze image features and labels that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. Whereas making memorable images is a challenging task in visualization and photography, this work is a first attempt to quantify this useful quality of images. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CVPR.2011.5995721 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
internet,computer vision,data visualisation,image classification,image retrieval,prediction theory,probability,visual databases,internet browsing,computer vision technique,data visualization,global image descriptor,image database,image memorability prediction,image quality,photography,visual information overflow,semantics,games,visualization,image features,correlation | Computer vision,Data visualization,Information retrieval,Computer science,Feature (computer vision),Visualization,Image retrieval,Image quality,Photography,Artificial intelligence,Contextual image classification,The Internet | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4577-0394-2 | 64 | 3.34 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Phillip Isola | 1 | 979 | 42.04 |
| Jianxiong Xiao | 2 | 2321 | 94.02 |
| Antonio Torralba | 3 | 14607 | 956.27 |
| Aude Oliva | 4 | 5121 | 298.19 |