Memorability measures how easily an image is to be memorized after glancing, which may contribute to designing magazine covers, tourism publicity materials, and so forth. Recent works have shed light on the visual features that make generic images, object images or face photographs memorable. However, a clear understanding and reliable estimation of natural scene memorability remain elusive. In this paper, we provide an attempt to answer: "what exactly makes natural scene memorable". To this end, we first establish a large-scale natural scene image memorability (LNSIM) database, containing 2,632 natural scene images and their ground truth memorability scores. Then, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of natural scene. In particular, we find that high-level feature of scene category is rather correlated with natural scene memorability. We also find that deep feature is effective in predicting the memorability scores. Therefore, we propose a deep neural network based natural scene memorability (DeepNSM) predictor, which takes advantage of scene category. Finally, the experimental results validate the effectiveness of our DeepNSM, exceeding the state-of-the-art methods.
Isola @cite_38 pioneered on the study of image memorability for generic images, and they have shown that memorability is an intrinsic property of an image. They further analyzed how various visual factors influence the memorability of generic images, and a greedy feature-selection scheme was employed to obtain the best feature combination for memorability prediction @cite_21 . The global features (i.e., pixels, GIST @cite_10 , SIFT @cite_18 , HOG2 @math 2 @cite_12 @cite_34 ) were verified and then combined. As a result, the accurate prediction performance of memorability is achieved on generic images @cite_38 @cite_21 @cite_3 .
@cite_38 @cite_18 @cite_21 @cite_3 @cite_34 @cite_10 @cite_12
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Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem. Experiment result shows that the proposed computational model obtains better prediction performance than the state of the art, and even outperforms human consistency. We further investigate the genericity of our model on other memorability datasets. Finally, by validating the model on interestingness datasets, we reconfirm the uncorrelation between memorability and interestingness of images. Recent studies in image memorability showed that the memorability of an image is a measurable quantity and is closely correlated with semantic attributes. However, the intrinsic characteristics of memorability are not yet fully understood. It has been reported that in contrast to a popular belief unusualness or aesthetic beauty of the image may not be positively correlated with the image memorability. This counter-intuitive characteristic of memorability hinders a better understanding of image memorability and its applicability. In this paper, we investigate two new spatial features that are closely correlated with the image memorability yet intuitively explainable. We propose the Weighted Object Area (WOA) that jointly considers the location and size of objects and the Relative Area Rank (RAR) that captures the relative unusualness of the size of objects. We empirically demonstrate their useful correlation with the image memorability. Results show that both WOA and RAR can improve the memorability prediction. In addition, we provide evidence that the RAR can effectively capture object-centric unusualness of size. When glancing at a magazine, or browsing the Internet, we are continuously exposed to photographs. Despite 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 stick in our minds while others are quickly forgotten. In this paper, we focus on the problem of predicting how memorable an image will be. We show that memorability is an intrinsic and stable property of an image that is shared across different viewers, and remains stable across delays. 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. While making memorable images is a challenging task in visualization, photography, and education, this work is a first attempt to quantify this useful property of images. Recent studies on image memorability have shed light on what distinguishes the memorability of different images and the intrinsic and extrinsic properties that make those images memorable. However, a clear understanding of the memorability of specific objects inside an image remains elusive. In this paper, we provide the first attempt to answer the question: what exactly is remembered about an image? We augment both the images and object segmentations from the PASCAL-S dataset with ground truth memorability scores and shed light on the various factors and properties that make an object memorable (or forgettable) to humans. We analyze various visual factors that may influence object memorability (e.g. color, visual saliency, and object categories). We also study the correlation between object and image memorability and find that image memorability is greatly affected by the memorability of its most memorable object. Lastly, we explore the effectiveness of deep learning and other computational approaches in predicting object memorability in images. Our efforts offer a deeper understanding of memorability in general thereby opening up avenues for a wide variety of applications.