Abstract
In this paper, we propose a model for semantic-based image retrieval (SBIR) on the clustering balanced tree, C-Tree, and ontology to analyze the semantics of an image and extract a similar set of images, in which the input is a query image. This structure is constructed rely on separating the nodes from the leaf node and growing towards the root to create a balanced tree. A set of similar images are searched on the C-Tree to classify the query image based on the k-NN (k-Nearest Neighbor) algorithm. Then, the SPARQL query is generated to query the semantics of the image on ontology. We experimented with image datasets such as COREL (1000 images), Wang (10,800 images), ImageCLEF (20,000 images). The results are compared and evaluated with the relevant projects published recently on the same datasets.
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Acknowledgment
The authors would like to thank the Faculty of Information Technology, University of Science - Hue University for their professional advice for this study. This work has been sponsored and funded by Ho Chi Minh City University of Food Industry under Contract No. 147/HD-DCT.
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Nhi, N.T.U., Van, T.T., Le, T.M. (2021). Semantic-Based Image Retrieval Using Balanced Clustering Tree. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_40
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