Graph-based semisupervised and manifold learning for image retrieval with SVM-based relevant feedback

Đã xuất bản năm 2019

Tác giả: Nguyễn Hữu Quỳnh, 01180006, Đào Thị Thúy Quỳnh, Ngô Quốc Tạo, Phương Văn Cảnh
Tạp chí: Journal of Intelligent & Fuzzy Systems
ISSN: 1064-1246
Xếp loại: SCIE
Fulltext: Chưa có

Tóm tắt bằng tiếng việt :

Tóm tắt bằng tiếng anh :

Over the years, many content-based image retrieval (CBIR) methods, which use SVM-based relevant feedback, are proposed to improve the performance of image retrieval systems. However, the performance of these methods is low due to the following limitations: (1) ignore the unlabeled samples; (2) only exploit the global Euclidean structure and (3) not taking advantage of the various useful aspects of the object. In order to solve the first problem, we propose a graph-based semisupervised learning (GSEL), which can add positive samples and construct balanced sets. With the second problem, we propose a manifold learning for dimensional reduction (MAL), which exploits the geometric properties of the manifold data. With the third problem, we propose a combination of classifiers by aspect (CCA), which exploits the various useful aspects of the object. Experimental results reported in the Corel Photo Gallery (with 31,695 images), which demonstrate the accuracy of our proposed method in improving the performance of the content-based image retrieval system. Keywords: Keyword one, keyword two, keyword three, keyword four, keyword five