A SELF-BALANCED CLUSTERING TREE FOR SEMANTIC-BASED IMAGE RETRIEVAL

Authors

  • Nguyen Thi Uyen Nhi - Faculty of Information Technology, University of Science - Hue University, Vietnam - Faculty of Information Technology, Sai Gon University, Vietnam
  • Van The Thanh Office of Scientific Research Management and Postgraduate Affairs, HCMC University of Food Industry, Vietnam
  • Le Manh Thanh Faculty of Information Technology, University of Science - Hue University, Vietnam

DOI:

https://doi.org/10.15625/1813-9663/36/1/14347

Keywords:

SBIR, image retrieval, similar image, C-Tree, Ontology

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.

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Published

2020-02-27

How to Cite

[1]
N. T. U. Nhi, V. T. Thanh, and L. M. Thanh, “A SELF-BALANCED CLUSTERING TREE FOR SEMANTIC-BASED IMAGE RETRIEVAL”, JCC, vol. 36, no. 1, p. 49–67, Feb. 2020.

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