Semi-supervised manifold ordinal regression for image ranking

Yang Liu; Yan Lu; Shenghua Zhong; Chun Chung Chan

doi:10.1145/2072298.2072023

Semi-supervised manifold ordinal regression for image ranking

Yang Liu, Yan Lu, Shenghua Zhong, Chun Chung Chan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

28 Citations (Scopus)

Abstract

In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.

Original language	English
Title of host publication	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
Pages	1393-1396
Number of pages	4
DOIs	https://doi.org/10.1145/2072298.2072023
Publication status	Published - 29 Dec 2011
Event	19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States Duration: 28 Nov 2011 → 1 Dec 2011

Conference

Conference	19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11
Country/Territory	United States
City	Scottsdale, AZ
Period	28/11/11 → 1/12/11

Keywords

Image ranking
Manifold learning
Manifold ordinal regression
Ordinal regression
Semi-supervised learning
Semi-supervised manifold ordinal regression

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Human-Computer Interaction

More information

10.1145/2072298.2072023

Cite this

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title = "Semi-supervised manifold ordinal regression for image ranking",

abstract = "In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.",

keywords = "Image ranking, Manifold learning, Manifold ordinal regression, Ordinal regression, Semi-supervised learning, Semi-supervised manifold ordinal regression",

author = "Yang Liu and Yan Lu and Shenghua Zhong and Chan, {Chun Chung}",

year = "2011",

month = dec,

day = "29",

doi = "10.1145/2072298.2072023",

language = "English",

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pages = "1393--1396",

booktitle = "MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops",

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T1 - Semi-supervised manifold ordinal regression for image ranking

AU - Liu, Yang

AU - Lu, Yan

AU - Zhong, Shenghua

AU - Chan, Chun Chung

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Y1 - 2011/12/29

N2 - In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.

AB - In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.

KW - Image ranking

KW - Manifold learning

KW - Manifold ordinal regression

KW - Ordinal regression

KW - Semi-supervised learning

KW - Semi-supervised manifold ordinal regression

UR - http://www.scopus.com/inward/record.url?scp=84455201904&partnerID=8YFLogxK

U2 - 10.1145/2072298.2072023

DO - 10.1145/2072298.2072023

M3 - Conference article published in proceeding or book

SN - 9781450306164

SP - 1393

EP - 1396

BT - MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

T2 - 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

Y2 - 28 November 2011 through 1 December 2011

ER -

Semi-supervised manifold ordinal regression for image ranking

Abstract

Conference

Keywords

ASJC Scopus subject areas

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