Weighted subspace filtering and ranking algorithms for video concept retrieval

Lin Lin; Chao Chen; Mei Ling Shyu; Shu Ching Chen

doi:10.1109/MMUL.2011.35

Weighted subspace filtering and ranking algorithms for video concept retrieval

Lin Lin, Chao Chen, Mei Ling Shyu, Shu Ching Chen

Electrical and Computer Engineering

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

38 Scopus citations

Abstract

The proposed framework, with weighted subspace filtering and ranking components, is the first attempt in multimedia research to apply multiple correspondence analysis to selected features while pruning data instances.

Original language	English (US)
Article number	5765919
Pages (from-to)	32-42
Number of pages	11
Journal	IEEE Multimedia
Volume	18
Issue number	3
DOIs	https://doi.org/10.1109/MMUL.2011.35
State	Published - Jul 2011

Keywords

concept retrieval
dissimilarity measure
filtering
multiple correspondence analysis (MCA)
ranking

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Science Applications
Signal Processing
Media Technology

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10.1109/MMUL.2011.35

Cite this

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title = "Weighted subspace filtering and ranking algorithms for video concept retrieval",

abstract = "The proposed framework, with weighted subspace filtering and ranking components, is the first attempt in multimedia research to apply multiple correspondence analysis to selected features while pruning data instances.",

keywords = "concept retrieval, dissimilarity measure, filtering, multiple correspondence analysis (MCA), ranking",

author = "Lin Lin and Chao Chen and Shyu, {Mei Ling} and Chen, {Shu Ching}",

note = "Funding Information: This work was supported in part by NSF HRD-0833093.",

year = "2011",

month = jul,

doi = "10.1109/MMUL.2011.35",

language = "English (US)",

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AU - Lin, Lin

AU - Chen, Chao

AU - Shyu, Mei Ling

AU - Chen, Shu Ching

N1 - Funding Information: This work was supported in part by NSF HRD-0833093.

PY - 2011/7

Y1 - 2011/7

N2 - The proposed framework, with weighted subspace filtering and ranking components, is the first attempt in multimedia research to apply multiple correspondence analysis to selected features while pruning data instances.

AB - The proposed framework, with weighted subspace filtering and ranking components, is the first attempt in multimedia research to apply multiple correspondence analysis to selected features while pruning data instances.

KW - concept retrieval

KW - dissimilarity measure

KW - filtering

KW - multiple correspondence analysis (MCA)

KW - ranking

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JF - IEEE Multimedia

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Weighted subspace filtering and ranking algorithms for video concept retrieval

Abstract

Keywords

ASJC Scopus subject areas

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