Image Memorability Prediction Using Depth and Motion Cues

Abstract

Memorability is one of the intrinsic image properties, which enables one to quantify to what extent images are memorable to the human cognitive system. Many works have shed light on various visual factors that influence the image memorability. Recently, one such study showed the influence of image depth and motion information on image memorability [3] . Based on these findings, this article proposes a deep learning-based prediction model, which utilizes depth and motion cues to predict the image memorability scores. The proposed model contains three deep CNN networks. Each of these three networks is individually trained to utilize one of the three visual factors: 1) visual depth information; 2) optical flow information; and 3) fine-tuned scene- and object-related features. In the end, all three networks are ensembled to predict the final memorability scores for the given image. An extensive set of experiments are conducted on large scale image memorability data set LaMem. From the experimental results, it is observed that the proposed model performs better than the current state-of-the-art model [3] by 2.44%.