However, not all images are equally memorable. For the new study, the researchers built a collection of about 10, images of all kinds -- interior-design photos, nature scenes, streetscapes and others. Human subjects in the study who participated through Amazon's Mechanical Turk program, which farms tasks out to people sitting at their own computers were shown a series of images, some of which were repeated.
Their task was to indicate, by pressing a key on their keyboard, when an image appeared that they had already seen. Each image's memorability rating was determined by how many participants correctly remembered seeing it. In general, different research subjects tended to produce similar memorability ratings.
After gathering their data, the researchers made "memorability maps" of each image by asking people to label all the objects in the images. A computer model can then analyze those maps to determine which objects make an image memorable. Alexei Efros, associate professor of computer science at Carnegie Mellon University, says the study offers a novel way to characterize images. But all of those questions are really hard to answer," says Efros, who was not involved in this research.
The researchers then used machine-learning techniques a type of statistical analysis that allows computers to identify patterns in data to create a computational model that analyzed the images and their memorability as rated by humans. 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.
Isola, P. What makes an image memorable? To examine this idea, we developed a method for creating automated memorability maps that display which local information in an image is memorable and which is forgettable. Predicting image memorability lends itself to a wide variety of applications.
We live in an age of data deluge, and memorability prediction could provide a method for summarizing and condensing the onslaught of visual data we encounter. For example, a photo album could be summarized using a few memorable photographs that convey the overall story. In education, textbook diagrams could be created to stick in students' minds, teachers could select memorable examples to illustrate concepts, and memorable cartoons could be used as mnemonic aids to make learning easier.
Memorability could also find applications in user-interface design. For example, memorable icons could clarify a messy desktop, and mnemonic labels could be attached to pill containers or entryways in retirement homes.
In addition, understanding memorability might lead to intelligent systems that preferentially store information based on its memorability, making sure to prioritize important information that humans will likely forget. Memorability research could be especially applicable within the domain of face memorability.
Indeed, in future work, we will be looking into algorithms that enable us to modify a portrait in subtle ways to enhance or reduce its memorability, while maintaining other facial traits like identity, attractiveness, and facial expression. Perhaps within the next few years smartphone applications will be developed that can select the most memorable photograph for a profile picture or that can help you apply makeup to boost your memorability.
Additionally, therapeutic technologies could be realized to train people to focus on key memorability-determining facial features to help those with social processing and memory-related disorders, such as autism, prosopagnosia, or Alzheimer's disease. A common factor across disciplines, memorability represents a fairly general quantification of the utility of visual information. Memorability varies from image to image, yet remains largely constant across multiple people viewing the same picture.
With this base understanding of memorability in place, our work might encourage machine vision and artificial intelligence researchers to consider not only what the world is about, but what humans consider meaningful: what they remember.
Her research lies at the interface between human perception, cognition, neuroscience, and computer vision. He works on human and computer vision, and is the recipient of a National Science Foundation Graduate Research Fellowship.
He is the recipient of the Facebook Fellowship — Wilma A. Forum in focus. Read more about this project. Explore context. Explore the latest strategic trends, research and analysis. How it works The team previously developed a similar algorithm for facial memorability. Publication does not imply endorsement of views by the World Economic Forum. License and Republishing. Written by.
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