Feature Line-Based Local Discriminant Analysis for Image Feature Extraction

Jeng-Shyang Pan; Shu-Chuan Chu; Lijun Yan

doi:10.1007/978-3-319-07773-4_46

Feature Line-Based Local Discriminant Analysis for Image Feature Extraction

Jeng-Shyang Pan, Shu-Chuan Chu, Lijun Yan

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, a novel image feature extraction algorithm, entitled Feature Line-based Local Discriminant Analysis (FLLDA), is proposed. FLLDA is a subspace learning algorithm based on Feature Line (FL) metric. FL metric is used for the evaluation of the local withinclass scatter and local between class scatter in the proposed FLLDA approach. The Experimental results on COIL20 image database confirm the effectiveness of the proposed algorithm.

Original language	English
Pages	471-478
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-319-07773-4_46
Publication status	Published - 1 Jan 2014
Event	The First Euro-China Conference on Intelligent Data Analysis and Applications - Shenzhen, China Duration: 13 Jun 2014 → 15 Jun 2014

Conference

Conference	The First Euro-China Conference on Intelligent Data Analysis and Applications
Country/Territory	China
City	Shenzhen
Period	13/06/14 → 15/06/14

Keywords

Feature extraction
Image classification
Line
Nearest feature

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10.1007/978-3-319-07773-4_46

Cite this

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title = "Feature Line-Based Local Discriminant Analysis for Image Feature Extraction",

abstract = "In this paper, a novel image feature extraction algorithm, entitled Feature Line-based Local Discriminant Analysis (FLLDA), is proposed. FLLDA is a subspace learning algorithm based on Feature Line (FL) metric. FL metric is used for the evaluation of the local withinclass scatter and local between class scatter in the proposed FLLDA approach. The Experimental results on COIL20 image database confirm the effectiveness of the proposed algorithm.",

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author = "Jeng-Shyang Pan and Shu-Chuan Chu and Lijun Yan",

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AU - Chu, Shu-Chuan

AU - Yan, Lijun

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Y1 - 2014/1/1

N2 - In this paper, a novel image feature extraction algorithm, entitled Feature Line-based Local Discriminant Analysis (FLLDA), is proposed. FLLDA is a subspace learning algorithm based on Feature Line (FL) metric. FL metric is used for the evaluation of the local withinclass scatter and local between class scatter in the proposed FLLDA approach. The Experimental results on COIL20 image database confirm the effectiveness of the proposed algorithm.

AB - In this paper, a novel image feature extraction algorithm, entitled Feature Line-based Local Discriminant Analysis (FLLDA), is proposed. FLLDA is a subspace learning algorithm based on Feature Line (FL) metric. FL metric is used for the evaluation of the local withinclass scatter and local between class scatter in the proposed FLLDA approach. The Experimental results on COIL20 image database confirm the effectiveness of the proposed algorithm.

KW - Feature extraction

KW - Image classification

KW - Line

KW - Nearest feature

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DO - 10.1007/978-3-319-07773-4_46

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T2 - The First Euro-China Conference on Intelligent Data Analysis and Applications

Y2 - 13 June 2014 through 15 June 2014

ER -

Feature Line-Based Local Discriminant Analysis for Image Feature Extraction

Abstract

Conference

Keywords

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