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论文类型：期刊论文

发表时间：2015-01-05

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI

卷号：147

期号：1

页面范围：509-522

ISSN号：0925-2312

关键字：Dimensionality reduction; Local curvature; Tangent space; CBIR; RF

摘要：At present, relevance feedback (RF) has been widely applied in content-based image retrieval (CBIR) system. Local Regression and Global Alignment (LRGA) is a novel ranking algorithm used in CBIR system which utilizes RE technique. However, there are some problems in LRGA: (1) for handling the problem of out-of-sample, dimension reduction is used after RF, but it is time-consuming; (2) feature space of images is often assumed to be linear. While, classical manifold learning methods are sensitive to the Gaussian bandwidth parameter of Laplacian matrix and cannot be combined with RF either. To address problems above, this paper proposes a novel CBIR system. Firstly, we calculate the local curvature parameter of manifold utilizing the angle information in subspace to avoid local high curvature problem and then we propose a Warp Linear Local Tangent Space Alignment (WLLTSA) algorithm; furthermore, we propose a U-Local Regression and Global Alignment (ULRGA) ranking algorithm to rank low-dimensional image features. Curvature parameter is used in both WLLTSA and ULRGA to enhance robustness. A large amount of experimental results demonstrate the efficiency of our CBIR system. (C) 2014 Elsevier B.V. All rights reserved.