Predicting media interestingness via biased discriminant embedding and supervised manifold regression

Yang Liu; Zhonglei Gu; Tobey H. Ko

Predicting media interestingness via biased discriminant embedding and supervised manifold regression

Yang Liu, Zhonglei Gu, Tobey H. Ko

Department of Computer Science

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

4 Citations (Scopus)

Abstract

In this paper, we describe our model designed for automatic prediction of media interestingness. Specifically, a two-stage learning framework is proposed. In the first stage, supervised dimensionality reduction is employed to discover the key discriminant information embedded in the original feature space. We present a new algorithm dubbed biased discriminant embedding (BDE) to extract discriminant features with discrete labels and use supervised manifold regression (SMR) to extract discriminant features with continuous labels. In the second stage, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.

Original language	English
Title of host publication	Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017)
Editors	Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J. F. Jones, Martha Larson
Publisher	CEUR-WS
Number of pages	3
Publication status	Published - Sept 2017
Event	2017 Multimedia Benchmark Workshop, MediaEval 2017 - Dublin, Ireland Duration: 13 Sept 2017 → 15 Sept 2017 https://ceur-ws.org/Vol-1984/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
Volume	1984
ISSN (Print)	1613-0073

Conference

Conference	2017 Multimedia Benchmark Workshop, MediaEval 2017
Country/Territory	Ireland
City	Dublin
Period	13/09/17 → 15/09/17
Internet address	https://ceur-ws.org/Vol-1984/

Scopus Subject Areas

General Computer Science

Access to Document

https://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_20.pdf

Cite this

Liu, Y., Gu, Z., & Ko, T. H. (2017). Predicting media interestingness via biased discriminant embedding and supervised manifold regression. In G. Gravier, B. Bischke, C.-H. Demarty, M. Zaharieva, M. Riegler, E. Dellandrea, D. Bogdanov, R. Sutcliffe, G. J. F. Jones, & M. Larson (Eds.), Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017) (CEUR Workshop Proceedings; Vol. 1984). CEUR-WS. https://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_20.pdf

Liu, Yang ; Gu, Zhonglei ; Ko, Tobey H. / Predicting media interestingness via biased discriminant embedding and supervised manifold regression. Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). editor / Guillaume Gravier ; Benjamin Bischke ; Claire-Hélène Demarty ; Maia Zaharieva ; Michael Riegler ; Emmanuel Dellandrea ; Dmitry Bogdanov ; Richard Sutcliffe ; Gareth J. F. Jones ; Martha Larson. CEUR-WS, 2017. (CEUR Workshop Proceedings).

@inproceedings{a83d773e82e64e6ab8ab007ebd07a242,

title = "Predicting media interestingness via biased discriminant embedding and supervised manifold regression",

abstract = "In this paper, we describe our model designed for automatic prediction of media interestingness. Specifically, a two-stage learning framework is proposed. In the first stage, supervised dimensionality reduction is employed to discover the key discriminant information embedded in the original feature space. We present a new algorithm dubbed biased discriminant embedding (BDE) to extract discriminant features with discrete labels and use supervised manifold regression (SMR) to extract discriminant features with continuous labels. In the second stage, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.",

author = "Yang Liu and Zhonglei Gu and Ko, {Tobey H.}",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61503317, and in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032.; 2017 Multimedia Benchmark Workshop, MediaEval 2017 ; Conference date: 13-09-2017 Through 15-09-2017",

year = "2017",

month = sep,

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

editor = "Guillaume Gravier and Benjamin Bischke and Claire-H{\'e}l{\`e}ne Demarty and Maia Zaharieva and Michael Riegler and Emmanuel Dellandrea and Dmitry Bogdanov and Richard Sutcliffe and Jones, {Gareth J. F.} and Martha Larson",

booktitle = "Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017)",

url = "https://ceur-ws.org/Vol-1984/",

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Liu, Y, Gu, Z & Ko, TH 2017, Predicting media interestingness via biased discriminant embedding and supervised manifold regression. in G Gravier, B Bischke, C-H Demarty, M Zaharieva, M Riegler, E Dellandrea, D Bogdanov, R Sutcliffe, GJF Jones & M Larson (eds), Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). CEUR Workshop Proceedings, vol. 1984, CEUR-WS, 2017 Multimedia Benchmark Workshop, MediaEval 2017, Dublin, Ireland, 13/09/17. <https://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_20.pdf>

Predicting media interestingness via biased discriminant embedding and supervised manifold regression. / Liu, Yang; Gu, Zhonglei; Ko, Tobey H.
Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). ed. / Guillaume Gravier; Benjamin Bischke; Claire-Hélène Demarty; Maia Zaharieva; Michael Riegler; Emmanuel Dellandrea; Dmitry Bogdanov; Richard Sutcliffe; Gareth J. F. Jones; Martha Larson. CEUR-WS, 2017. (CEUR Workshop Proceedings; Vol. 1984).

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

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N2 - In this paper, we describe our model designed for automatic prediction of media interestingness. Specifically, a two-stage learning framework is proposed. In the first stage, supervised dimensionality reduction is employed to discover the key discriminant information embedded in the original feature space. We present a new algorithm dubbed biased discriminant embedding (BDE) to extract discriminant features with discrete labels and use supervised manifold regression (SMR) to extract discriminant features with continuous labels. In the second stage, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.

AB - In this paper, we describe our model designed for automatic prediction of media interestingness. Specifically, a two-stage learning framework is proposed. In the first stage, supervised dimensionality reduction is employed to discover the key discriminant information embedded in the original feature space. We present a new algorithm dubbed biased discriminant embedding (BDE) to extract discriminant features with discrete labels and use supervised manifold regression (SMR) to extract discriminant features with continuous labels. In the second stage, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.

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BT - Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017)

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A2 - Bogdanov, Dmitry

A2 - Sutcliffe, Richard

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Liu Y, Gu Z, Ko TH. Predicting media interestingness via biased discriminant embedding and supervised manifold regression. In Gravier G, Bischke B, Demarty CH, Zaharieva M, Riegler M, Dellandrea E, Bogdanov D, Sutcliffe R, Jones GJF, Larson M, editors, Working Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017). CEUR-WS. 2017. (CEUR Workshop Proceedings).

Predicting media interestingness via biased discriminant embedding and supervised manifold regression

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