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.
Dubey @cite_29 were the first to study the problem of object memorability. They assumed that object categories play an important role in determining object memorability; therefore, they obtained the memorability scores of all constituent objects possibly appearing in object images by subjective experiment. Since the splendid performance of DNN is achieved in various recognition tasks, Dubey utilized the deep feature extracted by conv-net @cite_1 @cite_13 and ground truth scores of objects to predict object memorability better. Besides, @cite_6 learned video memorability from brain functional magnetic resonance imaging (fMRI).
@cite_13 @cite_29 @cite_1 @cite_6
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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. 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. Progress in estimating visual memorability has been limited by the small scale and lack of variety of benchmark data. Here, we introduce a novel experimental procedure to objectively measure human memory, allowing us to build LaMem, the largest annotated image memorability dataset to date (containing 60,000 images from diverse sources). Using Convolutional Neural Networks (CNNs), we show that fine-tuned deep features outperform all other features by a large margin, reaching a rank correlation of 0.64, near human consistency (0.68). Analysis of the responses of the high-level CNN layers shows which objects and regions are positively, and negatively, correlated with memorability, allowing us to create memorability maps for each image and provide a concrete method to perform image memorability manipulation. This work demonstrates that one can now robustly estimate the memorability of images from many different classes, positioning memorability and deep memorability features as prime candidates to estimate the utility of information for cognitive systems. Our model and data are available at: http: memorability.csail.mit.edu. Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects’ brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.