A fuzzy logic CBIR system

A fuzzy logic framework is proposed to alleviate two problems in traditional CBIR systems, including the semantic gap and the perception subjectivity. The proposed framework consists of two major parts, including (1) model construction and (2) query comparison. In the model construction part, fuzzy linguistic terms with associated fuzzy membership functions are automatically generated through an unsupervised fuzzy clustering algorithm. The linguistic terms provide a nature way of expressing user's concepts, and the membership functions characterize the mapping between image features and human visual concepts. We also define the syntax and semantics rules of a query description language to unify the query expression of textual descriptions, visual examples, and relevance feedbacks. In the query comparison part, a similarity function is inferred based on user's feedbacks to measure the similarity between the query and each image in the database. The user's preference is also captured and retained in his/her own profile to achieve personalization. Our work provides a unified and comprehensive framework for incorporation a fuzzy approach into CBIR systems. To verify our CBIR framework, we select Tamura features to describe and retrieve texture images. Experimental results show that the proposed framework is indeed effective to alleviate the semantic gap and the perception subjectivity problems.