Sentiment Flow for Video Interestingness Prediction

Abstract

Computational analysis and prediction of digital media interestingness is a challenging task, largely driven by subjective nature of interestingness. Several attempts were made to construct a reliable measure and obtain a better understanding of interestingness based on various psychological study results. However, most current works focus on interestingness prediction for images. While the video affective analysis has been studied for quite some time, there are few works that explictly try to predict interestingness of videos. In this work, we extend a recent pilot study on the video interestingness prediction by using a mid-level representation of sentiment (emotion) sequence. We evaluate our proposed framework on three datasets including the datasets proposed by the pilot study and show that the result effectively verifies a promising utility of the approach.