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Fuzzy Logic for Image Retrieval and Image Databases: A Literature Overview
Abstract
Fuzzy set theory has been extensively applied to the representation and processing of imprecise and uncertain data. Image data is becoming an important data resource with rapid growth in the number of large-scale image repositories. However, image data is fuzzy in nature, and imprecision and vagueness may exist in both image descriptions and query specifications. This chapter reviews some major work of image retrieval with fuzzy logic in the literature, including fuzzy content-based image retrieval and database support for fuzzy image retrieval. For the fuzzy content-based image retrieval, we present how fuzzy sets are applied for the extraction and representation of visual (colors, shapes, textures) features, similarity measures and indexing, relevance feedback, and retrieval systems. For the fuzzy image database retrieval, we present how fuzzy sets are applied for fuzzy image query processing based on a defined database models, and how various fuzzy database models can support image data management.
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