Highly Influenced. View 10 excerpts, cites background and methods. Modifying the Memorability of Face Photographs. View 10 excerpts, cites methods and background. Memorability is considered to be an important characteristic of visual content, whereas for advertisement and educational purposes it is often crucial.
Despite numerous studies on understanding and … Expand. View 5 excerpts, cites background and results. What Makes a Video Memorable? View 2 excerpts, cites background. The intrinsic memorability of face photographs.
View 1 excerpt, cites methods. Visual long-term memory has a massive storage capacity for object details. View 1 excerpt, references methods. View 1 excerpt, references background. Conceptual distinctiveness supports detailed visual long-term memory for real-world objects. 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 found that photographs with people or central objects were memorable, whereas landscapes—that one might expect to be memorable—were among the most forgettable see Figure 1. On average, 78 participants scored each image.
We then investigated the features of the more memorable images, including color, object statistics e. Using computer vision techniques, 1 we developed an image-ranking algorithm to automatically predict the memorability of images.
To do this, we trained a support vector regressor to map from features to memorability scores using only features algorithmically extracted from the images. The algorithm learned from the memorability scores calculated from the memory game.
We used half of the images in the database to train the algorithm and tested its performance with the remaining half. The algorithm correctly identified images with people as most memorable, indoor scenes and large objects as slightly less memorable, and outdoor landscapes as the least memorable. We have furthered this work by developing a framework for predicting image memorability that accounts for how the memorability of image regions and different types of features fade over time.
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.
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