What makes images memorable

The researchers are now doing a follow-up study to test longer-term memorability of images. They are also working on adding more detailed descriptions of image content, such as "two people shaking hands," or "people looking at each other," to each image's memorability map, in an effort to find out more about what makes the image memorable. Materials provided by Massachusetts Institute of Technology.

Note: Content may be edited for style and length. Science News. Why we remember Oliva's previous research has shown that the human brain can remember thousands of images, with a surprising level of detail. ScienceDaily, 24 May Massachusetts Institute of Technology. What makes an image memorable?.

Retrieved November 10, from www. Now neuroscientists have Image memorability dataset. Includes target and filler images, precomputed features and annotations, and memorability measurements from our "Memory Game". Code for computing features of new images, predicting their memorability, and replicating our results. We would like to thank Timothy Brady and Talia Konkle for helpful discussions and advice.

This is remarkable for a few reasons, not least of which is the tenuousness of the very concept of memorability. How do you begin to teach a machine to analyze an image for how memorable it will be to humans?

First you need to find out, from the humans themselves, what images are most memorable. So the researchers had people them play a game in which they were presented with a series of images, some of which were eventually repeated. The dataset was huge: It included some 60, diverse images—sunsets, clocks, ballerinas, selfies, dogs, trees, etc. Then, the researchers trained a computer, using a model they call MemNet, to classify those same images 1, different ways, based on what humans found memorable.

In other words, once they knew the machine could accurately predict what humans found memorable, they were able to figure out why. We introduce a database for which we have measured the probability that each picture will be recognized after a single view.

We analyze a collection of image features, labels, and attributes 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.