Semantic Image Annotation using Ontology And SPARQL
A. Gauthami Latha1, Ch. Satyanarayana2, Y. Srinivas3

1A. Gautami Latha*, CSE, SWEC, Hyderabad, India.
2Dr. Ch. Satyanarayana, CSE, JNTUK University, Kakinada, India.
3Dr. Y. Srinivas, IT, GITAM University, Vizag, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 3363-3368 | Volume-9 Issue-3, January 2020. | Retrieval Number: H7062068819/2020©BEIESP | DOI: 10.35940/ijitee.H7062.019320
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Abstract: Based on user’s interest or requirements, the search and retrieve images from large scale the databases, the contentbased image retrieval (CBIR) technique has become the primary emerging area in research for digital image processing which makes the visual contents to use. Most promising tools for image searching are Google Images and Yahoo Image search. They are used for annotations based on textual of the images. In this, the images are annotated manually with the help of keywords and then the retrieval is carried by using various search methods based on text. Due to this method, the system performance is too low. Therefore, CBIR goal is to construct Image Ontology. The Ontology extracts the relevant images from the database by using low-level features like texture, shape and color. In multimedia technology, the challenging task is to retrieve the relevant images from an image database. For representation, organization and retrieving of images, the searching approaches based on semantic provide effective and efficient results by using image ontology. In this paper, protege software shows us how to create ontology and SPARQL query language provides semantic annotation for images. In addition to this, Onto Viz and Onto Graph were used to generate Ontology in a graphical form for the relevant application. 
Keywords: Image Annotation, Ontology, Onto Graph, Onto Viz, OWL, Protégé Semantic, and SPARQL.
Scope of the Article: Image analysis and Processing