A Novel Video Content Classification Algorithm Based on Combined Visual Features Model

Author

Jiang, Xinghao ; Sun, Tanfeng ; Chen, Bin

Author_Institution

Sch. of Inf. Security Eng., Shanghai Jiaotong Univ., Shanghai, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

6

Abstract

This paper exploits the visual differences of five video genres, presents a combined model of editing, color, texture and motion features that could best distinguish one from the other, and uses the modified directed acyclic graph support vector machine (DAGSVM) model as the classifier. Experiment shows that: the features extracted have improved the identifiability of different genres, and computational complexity has been reduced; by introducing the DAG policy, the performance of the classifier has been enhanced; result demonstrates the precision and effectiveness of this approach, comparing with two other methods.

Keywords

feature extraction; image classification; image colour analysis; image texture; video signal processing; color; directed acyclic graph support vector machine; feature extraction; motion features; texture; video content classification algorithm; visual features model; Classification algorithms; Data analysis; Data mining; Feature extraction; Hidden Markov models; Information security; Machine learning algorithms; Sun; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing, 2009. CISP '09. 2nd International Congress on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4129-7

Electronic_ISBN

978-1-4244-4131-0

Type

conf

DOI

10.1109/CISP.2009.5305834

Filename

5305834