Automatic affective video indexing: Sound energy and object motion correlation discovery

Abstract

No longer is video creation and storage solely in the hands of professionals. Video repositories are growing at an astounding rate due advances in multimedia technologies. The vast size of video repositories presents challenges for users attempting to identify preferred content. Automated methods for content discovery are necessary to meet the needs of users. One of the more challenging areas of video content discovery is in identifying affective, or emotional, video content. Automatic affective video indexing techniques attempt to use computer-based methods to automatically identify content in videos that is affective in nature. This is the first known automatic affective video indexing study that focuses on slapstick, one of the most popular types of humor techniques. The study shows positive results and contributes to the field by identifying the targeted affective content without relying on actual human emotional responses.