Biased discriminant analysis with feature line embedding for relevance feedback-based image retrieval

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/101514

Title:	Biased discriminant analysis with feature line embedding for relevance feedback-based image retrieval
Authors:	陳映濃;Wang, Yu-Chen;Han, Chin-Chuan;Hsieh, Chen-Ta;Chen, Ying-Nong;Fan, Kuo-Chin
Contributors:	太空及遙測研究中心
Keywords:	Algorithm design and analysis;Biased discriminant analysis;content-based image retrieval (CBIR);feature line embedding (FLE);high-level semantic concept;Image retrieval;Learning systems;Linear programming;low-level image features;relevance feedback;Semantics
Date:	2015-12-01
Issue Date:	2026-04-21 14:36:48 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers Inc.;Piscataway: IEEE
Abstract:	摘要： The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. 其他題名： TMM 出版者： Piscataway: IEEE 出版日期： 2015-12 出處： IEEE transactions on multimedia, 2015-12, Vol.17 (12), p.2245-2258 資源來源： IEEE Electronic Library (IEL) 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2015 識別號： ISSN: 1520-9210 識別號： EISSN: 1941-0077 識別號： DOI: 10.1109/TMM.2015.2492926 識別號： CODEN: ITMUF8
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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