Image retrieval based on fuzzy ontology

Rapid increase in digital images demands effective and efficient image retrieval systems. In text based image retrieval, images are annotated with keywords based on human perception. A user query is composed of keywords according to his/her requirements. Query keywords are matched with the keywords associated with images, for retrieval. This process has been extended with ontology to resolve semantic heterogeneities. However, crisp annotation and retrieval processes could not produce the desired results because both processes involve human perception. To overcome this problem, we have proposed a retrieval system that makes use of fuzzy ontology for improving retrieval performance. For modeling the semantic description of an image, it is divided into regions in our dataset and then regions are classified into concepts. The concepts are combined into categories. The concepts, categories and images are linked among themselves with fuzzy values in ontology. The retrieved results are ranked based on the relevancy between the keywords of a query and images. For evaluating the performance of the proposed methodology, we have used both the objective and subjective measures. Experimental results show that the proposed system performs better than the existing systems in terms of retrieval performance.