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

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

Nguyen Thi Uyen Nhi (University of Science, Hue University, Vietnam & University of Economics, The University of Danang, Vietnam), Thanh Manh Le (University of Science, Hue University, Vietnam), and Thanh The Van (HCMC University of Food Industry, Vietnam)
Copyright: © 2022 |Pages: 23
DOI: 10.4018/IJSWIS.295551
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Abstract

The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the combination of C-Tree, which was built in our previous work, and a neighbor graph (called Graph-CTree) to improve accuracy. The k-Nearest Neighbor (k-NN) algorithm is used to classify a set of similar images that are retrieved on Graph-CTree to create a set of visual words. An ontology framework for images is created semi-automatically. SPARQL query is automatically generated from visual words and retrieve on ontology for semantics image. The experiment was performed on image datasets, such as COREL, WANG, ImageCLEF, and Stanford Dogs, with precision values of 0.888473, 0.766473, 0.839814, and 0.826416, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed here.
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There are many techniques of semantic-based image retrieval that have been implemented in many digital systems. The state-of-the-art techniques for reducing the ‘semantic gap’ can be broadly categorized into two main categories: (1) using machine learning methods to associate low-level features with visual concepts to describe image objects (Jui et al., 2017; Barz et al.,2020); (2) using ontology to define high-level semantics (Allani et al., 2017; Gonçalves et al., 2018; Bouchakwa et al.,2020). Many systems use one or a combination of different techniques to perform high-level semantic-based image retrieval.

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