Feature Line-Based Local Discriminant Analysis for Image Feature Extraction

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

    Research output: Contribution to conferencePaper

    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 languageEnglish
    Pages471-478
    Number of pages8
    DOIs
    Publication statusPublished - 1 Jan 2014
    EventThe First Euro-China Conference on Intelligent Data Analysis and Applications - Shenzhen, China
    Duration: 13 Jun 201415 Jun 2014

    Conference

    ConferenceThe First Euro-China Conference on Intelligent Data Analysis and Applications
    CountryChina
    CityShenzhen
    Period13/06/1415/06/14

    Keywords

    • Feature extraction
    • Image classification
    • Line
    • Nearest feature

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  • Cite this

    Pan, J-S., Chu, S-C., & Yan, L. (2014). Feature Line-Based Local Discriminant Analysis for Image Feature Extraction. 471-478. Paper presented at The First Euro-China Conference on Intelligent Data Analysis and Applications, Shenzhen, China. https://doi.org/10.1007/978-3-319-07773-4_46