Latent topic driving model for movie affective scene classification

  • Authors:

  • Go Irie;Kota Hidaka;Takashi Satou;Akira Kojima;Toshihiko Yamasaki;Kiyoharu Aizawa

  • Affiliations:

  • NTT Corporation, Yokosuka, Japan;NTT East Corporation, Shinjyuku-ku, Japan;NTT Corporation, Yokosuka, Japan;NTT Corporation, Yokosuka, Japan;University of Tokyo, Bunkyo-ku, Japan;University of Tokyo, Bunkyo-ku, Japan

  • Venue:
  • MM '09 Proceedings of the 17th ACM international conference on Multimedia
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper proposes a latent topic driving model (LTDM) as a novel approach to movie affective scene classification. LTDM is a discriminative model of emotions driven by movie affective contents. Unlike existing methods, our approach is based on movie topic extraction via the latent Dirichlet allocation (LDA) and emotion dynamics modeling with reference to Plutchik's emotion theory. The classification procedure starts by segmenting movie scenes into movie shots, each of which is represented by a histogram of quantized affect-related audio-visual features. LDA is applied to detect topics of each movie shot. Emotions for the current movie shot are estimated based on both the topics of the shot and emotion transition weights determined by Plutchik's emotion theory. We conduct experiments using 206 movie scenes extracted from 24 movie titles (total 6 hours 20 min. 12 sec.) and the labels of eight emotion categories given by 16 subjects are collected. The results show that LTDM outperforms conventional modeling approaches in terms of the subject agreement rate.