Adaptive discriminative generativemodel and its applications

Ruei Sung Lin; David Ross; Jongwoo Lim; Ming Hsuan Yang

Adaptive discriminative generativemodel and its applications

Ruei Sung Lin, David Ross, Jongwoo Lim, Ming Hsuan Yang

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

Abstract

This paper presents an adaptive discriminative generativemodel that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update this discriminativemodel as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
Publisher	Neural information processing systems foundation
ISBN (Print)	0262195348, 9780262195348
Publication status	Published - 2005 Jan 1
Event	18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada Duration: 2004 Dec 13 → 2004 Dec 16

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Conference

Conference	18th Annual Conference on Neural Information Processing Systems, NIPS 2004
Country/Territory	Canada
City	Vancouver, BC
Period	04/12/13 → 04/12/16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Adaptive discriminative generativemodel and its applications",

abstract = "This paper presents an adaptive discriminative generativemodel that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update this discriminativemodel as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.",

author = "Lin, {Ruei Sung} and David Ross and Jongwoo Lim and Yang, {Ming Hsuan}",

year = "2005",

month = jan,

day = "1",

language = "English",

isbn = "0262195348",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004",

note = "18th Annual Conference on Neural Information Processing Systems, NIPS 2004 ; Conference date: 13-12-2004 Through 16-12-2004",

}

Lin, RS, Ross, D, Lim, J & Yang, MH 2005, Adaptive discriminative generativemodel and its applications. in Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 18th Annual Conference on Neural Information Processing Systems, NIPS 2004, Vancouver, BC, Canada, 04/12/13.

Adaptive discriminative generativemodel and its applications. / Lin, Ruei Sung; Ross, David; Lim, Jongwoo et al.
Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004. Neural information processing systems foundation, 2005. (Advances in Neural Information Processing Systems).

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

TY - GEN

T1 - Adaptive discriminative generativemodel and its applications

AU - Lin, Ruei Sung

AU - Ross, David

AU - Lim, Jongwoo

AU - Yang, Ming Hsuan

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N2 - This paper presents an adaptive discriminative generativemodel that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update this discriminativemodel as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.

AB - This paper presents an adaptive discriminative generativemodel that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update this discriminativemodel as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.

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M3 - Conference contribution

SN - 0262195348

SN - 9780262195348

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004

PB - Neural information processing systems foundation

T2 - 18th Annual Conference on Neural Information Processing Systems, NIPS 2004

Y2 - 13 December 2004 through 16 December 2004

ER -

Adaptive discriminative generativemodel and its applications

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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