A Self-Balanced Clustering Tree for Semantic-Based Image Retrieval

Abstract

The image retrieval and semantic extraction play an important role in the multimedia systems such as geographic information system, hospital information system, digital library system, etc. Therefore, the research and development of semantic-based image retrieval (SBIR) systems have become extremely important and urgent. Major recent publications are included covering different aspects of the research in this area, including building data models, low-level image feature extraction, and deriving high-level semantic features. However, there is still no general approach for semantic-based image retrieval (SBIR), due to the diversity and complexity of high-level semantics. In order to improve the retrieval accuracy of SBIR systems, our focus research is to build a data structure for finding similar images, from that retrieving its semantic. In this paper, we proposed a data structure which is a self-balanced clustering tree named C-Tree. Firstly, a method of visual semantic analysis relied on visual features and image content is proposed on C-Tree. The building of this structure is created based on a combination of methods including hierarchical clustering and partitional clustering. Secondly, we design ontology for the image dataset and create the SPARQL (SPARQL Protocol and RDF Query Language) query by extracting semantics of image. Finally, the semantic-based image retrieval on C-Tree (SBIR\_CT) model is created hinging on our proposal. The experimental evaluation 20,000 images of ImageCLEF dataset indicates the effectiveness of the proposed method. These results are compared with some of recently published methods on the same dataset and demonstrate that the proposed method improves the retrieval accuracy and efficiency.

A Model of Semantic-Based Image Retrieval Using C-Tree and Neighbor Graph

“…C-Tree (Nhi et al, 2020) consists of a root, a set of nodes I , and a set of leaves L . Nodes are connected through the path of the parent-child relationship.…”

“…The result of using SPARQL query on ontology is the set of descriptive semantics of the dataset and the set of similar images according to the semantic of the input image. A balanced clustering tree structure, called C-Tree, was published in Nhi et al (2020) to automatically archive indexed images from low-level features of the image. C-Tree has many advantages, such as a multi-branch tree and clustered feature vectors, so it can store large amounts of data and retrieve images rapidly.…”

Tìm kiếm ảnh dựa vào Ontology

“…Đồ thị cụm láng giềng được cải tiến từ cấu trúc cây phân cụm cân bằng C-Tree [18,19] đã được chúng tôi xây dựng. C-Tree có thể lưu trữ được dữ liệu lớn, hiệu quả cho bài toán tìm kiếm ảnh với thời gian tìm kiếm nhanh, độ chính xác khá cao.…”