A Study on Automatic Indexing of Image Contents Based on Semantic Field by Modal Logic

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The final purpose of this research project is to provide an automatic indexing system and its application to image content retrieval. When we consider image retrieval and indexing, we must consider two levels of both logical (linguistic) and physical (image). To connect the two levels we need some intermediate media, as which we adopt the theory of semantic field by modal logic, which was formerly proposed by Professor Germano Resconi from Italy and myself. The semantic field gives us a way of visual treatment of logical inference. The project contains the following two steps :(1)Indexing by semantic field as intermediate media and its application to image content retrieval(2)Automatic indexing of image content by logical language.For the purpose we develop a new theory of granularization based on semantic field in the project. The concept of granurality has recently put emphasis on in the research field of rough sets, which are algebraic counter parts of modal logics. In our theory of granularization, we define two mathematical operations of zooming in and out in a modal logical setting and then apply them to adjustment of granularity, which has an important role in image processing. The idea is originated in the so-called filtration method in modal logic, which is the basic way of decision problems. Then by regarding images as semantic fields on 2-dimensional Euclidean space, we can apply the two operations to images. As a result we can develop an image content retrieval system and make the basis of automatic indexing of image contents by a logical language. It is very important that we can point out that our way of indexing is very close to abductive reasoning.