Semi-supervised manifold ordinal regression for image ranking

Yang Liu; Yanqun Li Lu; Shenghua Zhong; Keith C.C. Chan

doi:10.1145/2072298.2072023

Semi-supervised manifold ordinal regression for image ranking

Yang Liu^*
, Yanqun Li Lu
, Shenghua Zhong
, Keith C.C. Chan

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

30 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 2011 - Proceedings of the 19th ACM international conference on Multimedia
Publisher	Association for Computing Machinery (ACM)
Pages	1393-1396
Number of pages	4
ISBN (Print)	9781450306164
DOIs	https://doi.org/10.1145/2072298.2072023
Publication status	Published - 28 Nov 2011
Event	19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States Duration: 28 Nov 2011 → 1 Dec 2011 https://dl.acm.org/doi/proceedings/10.1145/2072298 (Conference proceeding)

Publication series

Name	Proceedings of the ACM international conference on Multimedia

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
Internet address	https://dl.acm.org/doi/proceedings/10.1145/2072298 (Conference proceeding)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

User-Defined Keywords

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

Access to Document

10.1145/2072298.2072023

Cite this

@inproceedings{60e9e5a108024ec3bd4f7da041b9ce14,

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 Lu, \{Yanqun Li\} and Shenghua Zhong and Chan, \{Keith C.C.\}",

note = "This work was supported by grant PolyU 5204/09E. Publisher Copyright: Copyright 2011 ACM ; 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 ; Conference date: 28-11-2011 Through 01-12-2011",

year = "2011",

month = nov,

day = "28",

doi = "10.1145/2072298.2072023",

language = "English",

isbn = "9781450306164",

series = "Proceedings of the ACM international conference on Multimedia",

publisher = "Association for Computing Machinery (ACM)",

pages = "1393--1396",

booktitle = "MM 2011 - Proceedings of the 19th ACM international conference on Multimedia",

address = "United States",

url = "https://dl.acm.org/doi/proceedings/10.1145/2072298",

}

Liu, Y, Lu, YL, Zhong, S & Chan, KCC 2011, Semi-supervised manifold ordinal regression for image ranking. in MM 2011 - Proceedings of the 19th ACM international conference on Multimedia. Proceedings of the ACM international conference on Multimedia, Association for Computing Machinery (ACM), pp. 1393-1396, 19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11, Scottsdale, AZ, United States, 28/11/11. https://doi.org/10.1145/2072298.2072023

Semi-supervised manifold ordinal regression for image ranking. / Liu, Yang; Lu, Yanqun Li; Zhong, Shenghua et al.
MM 2011 - Proceedings of the 19th ACM international conference on Multimedia. Association for Computing Machinery (ACM), 2011. p. 1393-1396 (Proceedings of the ACM international conference on Multimedia).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Semi-supervised manifold ordinal regression for image ranking

AU - Liu, Yang

AU - Lu, Yanqun Li

AU - Zhong, Shenghua

AU - Chan, Keith C.C.

PY - 2011/11/28

Y1 - 2011/11/28

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 - https://www.scopus.com/pages/publications/84455201904

U2 - 10.1145/2072298.2072023

DO - 10.1145/2072298.2072023

M3 - Conference proceeding

AN - SCOPUS:84455201904

SN - 9781450306164

T3 - Proceedings of the ACM international conference on Multimedia

SP - 1393

EP - 1396

BT - MM 2011 - Proceedings of the 19th ACM international conference on Multimedia

PB - Association for Computing Machinery (ACM)

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

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UN SDGs

User-Defined Keywords

Access to Document

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