Image Retrieval Based on Fuzzy Set Theory and Development of an Experimental System.

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Recently, as the result of the development of the researches on multimedia databases and hypertexts, image retrieval is rapidly becoming more important technique in those fields. In the past, some methods have been used for the image retrieval. For example, the similar image retrieval method, in which a given picture is used for a query and the retrieved result is the most similar picture in a image database, and the method of using network index are well known. On image indexing and retrieving, the pattern recognition techniques and the products of cognitive science are basic and useful techniques generally. On the other hand, image data has some sort of uncertainty or fuzziness in itself and it is natural to apply fuzzy set theory to image retrieval, but the method of fuzzy image retrieval is not proposed yet.In this research, we proposed new method of image retrieval based on fuzzy set theory and implement an experimental image retrieval system. The summary of the research is shown below :1. The propositional network model was proposed for the image memory process of human beings. Based on this model, the set of fuzzy propositions are used as the image index.2. We assumed that the query is composed a set of fuzzy propositions that has the same structure of image database indexes. Using this type of query, we can make various user interfaces in which user query can be a set of keywords or an expression in natural language or a reference image.3. We proposed a fuzzy model for measuring a matching degree between a query and a index and produce an algorithm which calculates a matching degree efficiently. This degree is used as a fuzzy membership value on the retrieving process of the image database.4. We implemented an experimental image retrieval system using this method.