Thông tin chung
  English
  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác
  Tài liệu tham khảo
  Hiệu chỉnh
 
Số người truy cập: 107,427,693

 A SEMANTIC-BASED IMAGE RETRIEVAL SYSTEM USING A HYBRID METHOD K-MEANS AND K-NEAREST-NEIGHBOR
Tác giả hoặc Nhóm tác giả: Thanh, L. M., N. T. U. Nhi, N. T. U. Thi, P. T. A. Han and V. T. Thanh
Nơi đăng: Annales Univ. Sci. Budapest., Sect. Comp.; Số: 51;Từ->đến trang: 253-274;Năm: 2020
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Semantic-based image retrieval is one of the important problems for the multimedia system and has garnered great interest in recent years. This paper proposes an improved method to build a self-balanced clustering tree, called iC Tree. We build the Semantic-Based Image Retrieval (SBIR) based on iC-Tree named SBIR iCT. We propose a hydrid method, which is a combination of K-mean, K-Nearest-Neighbor (KNN) to build iC-Tree, to improve the classification performance of this tree. The system uses images that are segmented into different regions and extracted visual features of each region. Semantic concepts are analyzed from those features based on the proposed C-Tree. The result of the retrieval process is a set of similar images with semantic concepts, which matches the query image and a visual word vector. Then, we design ontology for the image dataset and create the SPARQL query by extracting semantics of image. Finally, the semantic-based image retrieval on iC-Tree (SBIR iCT) model is created hinging on our proposal. Experiments on 20,000 images of ImageCLEF dataset confirm that our proposed methods improve the retrieval performance, compared with an image retrieval system using K-means clutering, which we proposed earlier. At the same time, this performance is compared with some of recently published methods on the same dataset, show that our proposed methods are effective in handling problems of SBIR.
ABSTRACT
Semantic-based image retrieval is one of the important problems for the multimedia system and has garnered great interest in recent years. This paper proposes an improved method to build a self-balanced clustering tree, called iC Tree. We build the Semantic-Based Image Retrieval (SBIR) based on iC-Tree named SBIR iCT. We propose a hydrid method, which is a combination of K-mean, K-Nearest-Neighbor (KNN) to build iC-Tree, to improve the classification performance of this tree. The system uses images that are segmented into different regions and extracted visual features of each region. Semantic concepts are analyzed from those features based on the proposed C-Tree. The result of the retrieval process is a set of similar images with semantic concepts, which matches the query image and a visual word vector. Then, we design ontology for the image dataset and create the SPARQL query by extracting semantics of image. Finally, the semantic-based image retrieval on iC-Tree (SBIR iCT) model is created hinging on our proposal. Experiments on 20,000 images of ImageCLEF dataset confirm that our proposed methods improve the retrieval performance, compared with an image retrieval system using K-means clutering, which we proposed earlier. At the same time, this performance is compared with some of recently published methods on the same dataset, show that our proposed methods are effective in handling problems of SBIR.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn