AN APPROACH OF SEMANTIC-BASED IMAGE RETRIEVAL USING DEEP NEURAL NETWORK AND ONTOLOGY
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Abstract
Semantic extraction for images is a computational problem and is applied in many different semantic retrieval systems. In this paper, a semantic-based image retrieval approach is proposed based on images similar to the input image; since then, the semantic of the image is retrieved on the ontology through the set of visual words. The objects on each image are extracted and classified based on the CNN network to extract semantics for the image. Then, the command of SPARQL is automatically generated from the visual words of the image and executes the query on the built ontology for extracting corresponding semantics. The proposed base method, an experiment was built and evaluated on the Caltech-256 dataset. Experimental results are compared with recently published work on the same dataset results to demonstrate the effectiveness of the proposed method. According to the experimental results, the image semantic lookup method in this paper has increased the accuracy to 0.88712 for the Caltech-256 dataset.
Keywords
classification, CNN, Semantic-based Image Retrieval, ontology
Article Details
References
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