Affective Level Video Segmentation by Utilizing the Pleasure-Arousal-Dominance Information

In this paper, we offer an entirely new view to the problem of high level video parsing. We developed a novel computation method for affective level video segmentation. Its function was to extract emotional segments from videos. Its design was based on the pleasure-arousal-dominance (P-A-D) model of...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on multimedia 2008-11, Vol.10 (7), p.1325-1341
Main Authors: Arifin, S., Cheung, P.
Format: Article
Language:English
Subjects:
Acceleration
Affective video content analysis
Algorithms
Applied sciences
Artificial intelligence
Bayesian methods
Bayesian networks
Buildings
Clustering
Clustering algorithms
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data mining
Emotion recognition
Emotions
Exact sciences and technology
Indexing
Mathematical analysis
Multimedia
Pattern recognition. Digital image processing. Computational geometry
Performance analysis
Segmentation
Segments
Software
Spectra
Video on demand
video segmentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we offer an entirely new view to the problem of high level video parsing. We developed a novel computation method for affective level video segmentation. Its function was to extract emotional segments from videos. Its design was based on the pleasure-arousal-dominance (P-A-D) model of affect representation , which in principle can represent a large number of emotions. Our method had two stages. The first P-A-D estimation stage was defined within framework of the dynamic Bayesian networks (DBNs). A spectral clustering algorithm was applied in the final stage to determine the emotional segments of the video. The performance of our method was compared with the time adaptive clustering (TAC) algorithm and an accelerated version of it which we had developed. According to Vendrig , the TAC algorithm was the best segmentation method. Experiment results will show the feasibility of our method.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2008.2004911