Applying the multi-category learning to multiple video object extraction

Yi Liu; Yuan F. Zheng; Xiaotong Shen

doi:10.1016/j.patcog.2008.02.007

Applying the multi-category learning to multiple video object extraction

Yi Liu
, Yuan F. Zheng
, Xiaotong Shen

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Video object (VO) extraction is of great importance in multimedia processing. In recent years approaches have been proposed to deal with VO extraction as a classification problem. This type of methods calls for state-of-the-art classifiers because the performance is directly related to the accuracy of classification. Promising results have been reported for single object extraction using support vector machines (SVM) and its extensions. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an ongoing research topic in machine learning. This paper introduces a new scheme of multi-category learning for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments.

Original language	English (US)
Pages (from-to)	2777-2788
Number of pages	12
Journal	Pattern Recognition
Volume	41
Issue number	9
DOIs	https://doi.org/10.1016/j.patcog.2008.02.007
State	Published - Sep 2008

Bibliographical note

Funding Information:
This work was supported in part by the US National Science Foundation under Grant IIS-0328802, and in part by the Chinese Natural Science Foundation under Grant 60632040.

Keywords

Multi-class classification
Multiple object tracking
Support vector machines (SVM)
VO extraction
ψ-Learning

Publisher link

10.1016/j.patcog.2008.02.007

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title = "Applying the multi-category learning to multiple video object extraction",

abstract = "Video object (VO) extraction is of great importance in multimedia processing. In recent years approaches have been proposed to deal with VO extraction as a classification problem. This type of methods calls for state-of-the-art classifiers because the performance is directly related to the accuracy of classification. Promising results have been reported for single object extraction using support vector machines (SVM) and its extensions. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an ongoing research topic in machine learning. This paper introduces a new scheme of multi-category learning for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments.",

keywords = "Multi-class classification, Multiple object tracking, Support vector machines (SVM), VO extraction, ψ-Learning",

author = "Yi Liu and Zheng, \{Yuan F.\} and Xiaotong Shen",

note = "Funding Information: This work was supported in part by the US National Science Foundation under Grant IIS-0328802, and in part by the Chinese Natural Science Foundation under Grant 60632040. ",

year = "2008",

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doi = "10.1016/j.patcog.2008.02.007",

language = "English (US)",

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TY - JOUR

T1 - Applying the multi-category learning to multiple video object extraction

AU - Liu, Yi

AU - Zheng, Yuan F.

AU - Shen, Xiaotong

N1 - Funding Information: This work was supported in part by the US National Science Foundation under Grant IIS-0328802, and in part by the Chinese Natural Science Foundation under Grant 60632040.

PY - 2008/9

Y1 - 2008/9

N2 - Video object (VO) extraction is of great importance in multimedia processing. In recent years approaches have been proposed to deal with VO extraction as a classification problem. This type of methods calls for state-of-the-art classifiers because the performance is directly related to the accuracy of classification. Promising results have been reported for single object extraction using support vector machines (SVM) and its extensions. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an ongoing research topic in machine learning. This paper introduces a new scheme of multi-category learning for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments.

AB - Video object (VO) extraction is of great importance in multimedia processing. In recent years approaches have been proposed to deal with VO extraction as a classification problem. This type of methods calls for state-of-the-art classifiers because the performance is directly related to the accuracy of classification. Promising results have been reported for single object extraction using support vector machines (SVM) and its extensions. Multiple object extraction, on the other hand, still imposes great difficulty as multi-category classification is an ongoing research topic in machine learning. This paper introduces a new scheme of multi-category learning for multiple VO extraction, and demonstrates its effectiveness and advantages by experiments.

KW - Multi-class classification

KW - Multiple object tracking

KW - Support vector machines (SVM)

KW - VO extraction

KW - ψ-Learning

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DO - 10.1016/j.patcog.2008.02.007

M3 - Article

AN - SCOPUS:44649108872

SN - 0031-3203

VL - 41

SP - 2777

EP - 2788

JO - Pattern Recognition

JF - Pattern Recognition

IS - 9

ER -

Applying the multi-category learning to multiple video object extraction

Abstract

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Keywords

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