Video shot classification using human faces

Yin Chan; Shang Hung Lin; Yap Peng Tan; Sun-Yuan Kung

Video shot classification using human faces

Yin Chan, Shang Hung Lin, Yap Peng Tan, Sun-Yuan Kung

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

People usually make up a lot of information contents in videos. The abilities to answer queries and facilitate browsing related to people in videos are crucial. In a single video sequence, a particular person may appear multiple number of times. In this paper, we propose a scheme to automatically detect the repeated occurrences of the same people to enable fast people related searching. In particular, we propose a video shot classification scheme using human faces, regardless of scale and background. Video shots are classified by clustering facial features extracted from these shots. Potential applications include video indexing and browsing. Employing unsupervised clustering algorithms, this scheme requires no human intervention. Experimental results on a 4-minute news sequence show that it achieves encouraging results (cf. figures 4 & 5).

Original language	English (US)
Title of host publication	IEEE International Conference on Image Processing
Editors	Anon
Publisher	IEEE
Pages	843-846
Number of pages	4
Volume	3
State	Published - Dec 1 1996
Event	Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz Duration: Sep 16 1996 → Sep 19 1996

Other

Other	Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
City	Lausanne, Switz
Period	9/16/96 → 9/19/96

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Hardware and Architecture
Electrical and Electronic Engineering

Cite this

@inproceedings{972ddcb8f8894540806f15049a33a58a,

title = "Video shot classification using human faces",

abstract = "People usually make up a lot of information contents in videos. The abilities to answer queries and facilitate browsing related to people in videos are crucial. In a single video sequence, a particular person may appear multiple number of times. In this paper, we propose a scheme to automatically detect the repeated occurrences of the same people to enable fast people related searching. In particular, we propose a video shot classification scheme using human faces, regardless of scale and background. Video shots are classified by clustering facial features extracted from these shots. Potential applications include video indexing and browsing. Employing unsupervised clustering algorithms, this scheme requires no human intervention. Experimental results on a 4-minute news sequence show that it achieves encouraging results (cf. figures 4 & 5).",

author = "Yin Chan and Lin, {Shang Hung} and Tan, {Yap Peng} and Sun-Yuan Kung",

year = "1996",

month = dec,

day = "1",

language = "English (US)",

volume = "3",

pages = "843--846",

editor = "Anon",

booktitle = "IEEE International Conference on Image Processing",

publisher = "IEEE",

note = "Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) ; Conference date: 16-09-1996 Through 19-09-1996",

}

TY - GEN

T1 - Video shot classification using human faces

AU - Chan, Yin

AU - Lin, Shang Hung

AU - Tan, Yap Peng

AU - Kung, Sun-Yuan

PY - 1996/12/1

Y1 - 1996/12/1

N2 - People usually make up a lot of information contents in videos. The abilities to answer queries and facilitate browsing related to people in videos are crucial. In a single video sequence, a particular person may appear multiple number of times. In this paper, we propose a scheme to automatically detect the repeated occurrences of the same people to enable fast people related searching. In particular, we propose a video shot classification scheme using human faces, regardless of scale and background. Video shots are classified by clustering facial features extracted from these shots. Potential applications include video indexing and browsing. Employing unsupervised clustering algorithms, this scheme requires no human intervention. Experimental results on a 4-minute news sequence show that it achieves encouraging results (cf. figures 4 & 5).

AB - People usually make up a lot of information contents in videos. The abilities to answer queries and facilitate browsing related to people in videos are crucial. In a single video sequence, a particular person may appear multiple number of times. In this paper, we propose a scheme to automatically detect the repeated occurrences of the same people to enable fast people related searching. In particular, we propose a video shot classification scheme using human faces, regardless of scale and background. Video shots are classified by clustering facial features extracted from these shots. Potential applications include video indexing and browsing. Employing unsupervised clustering algorithms, this scheme requires no human intervention. Experimental results on a 4-minute news sequence show that it achieves encouraging results (cf. figures 4 & 5).

UR - http://www.scopus.com/inward/record.url?scp=0030410626&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030410626&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0030410626

VL - 3

SP - 843

EP - 846

BT - IEEE International Conference on Image Processing

A2 - Anon, null

PB - IEEE

T2 - Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)

Y2 - 16 September 1996 through 19 September 1996

ER -

Video shot classification using human faces

Abstract

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All Science Journal Classification (ASJC) codes

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