Automated Saliency Prediction in Cinema Studies

Abstract

Abstract In visual cognition research, saliency refers to the prominence of specific elements in a scene. Moreover, saliency guidance is part of a filmmaker's toolset to build narratives and guide the audience into emotive responses. This article compares two Convolutional Neural Network (CNN) saliency mapping models with viewers’ eye-position mapping to investigate the potentiality of automated saliency mapping in moving image studies by analyzing saliency's role during cinema's transition from one-shot to multiple-shot. Although the exact moment when montage and editing methods appeared cannot be identified with precision, findings suggest one of the reasons for this transition was saliency guidance, hence its preponderance.