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.
Highlights
The problems of image mining, image retrieval, and image semantics are becoming more popular among researchers
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
In this paper, we classify a set of similar images to generate a set of visual words. Because this image dataset has a small size, the use of a deep learning network in this step is not effective; we propose the k-NN algorithm, which has low computational complexity but still has high precision to classify the set of similar images
Summary
The problems of image mining, image retrieval, and image semantics are becoming more popular among researchers. Identifying the desired image from a large and diverse image dataset is a challenging task. It is essential to develop high-precision image retrieval systems for large datasets. The low-level content of images, including color, shape, and texture, cannot define the user’s high-level semantics (Allani et al, 2017; Cevikalp et al, 2018). It is essential to have a structure for mapping low-level features to high-level semantics based on ontology (Filali et al, 2017; Sarwara et al, 2013). High-level semantics of images can classify and retrieve high-level semantics of images based on SPARQL (Hogan, 2020)
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