Institute of Electrical and Electronics Engineers Inc.;Piscataway: IEEE
摘要:
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 Xplore Digital Library 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
