Semantic-based image retrieval has been one of the most challenging problems in recent years. Although so many solutions are provided for filling the so-called gap between the content based image retrieval (CBIR) and what human beings expect from the retrieval task; none of them yields satisfactory results and the problem is still open for further research. In this paper, type-2 fuzzy logic (T2FL) framework is considered to alleviate two problems in traditional CBIR systems, including the

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... ic gap and the perception subjectivity. Employing T2FL has the potential to overcome the limitations of type-1 fuzzy logic and produce a new generation of fuzzy controllers with improved performance for many CBIR applications that require handling high levels of uncertainty. Thus, our contributions in this study are threefold. (1) The proposed system maps low-level visual statistical features to high-level semantic concepts; enabling to retrieve and browse image collections by their high-level semantic concepts. (2) Type2 fuzzy logic has been used to fuse (combine) extracted features as well as to deal with the ambiguity of human judgment of image similarity. (3) The system models the human perception subjectivity with the ability to handle high levels of uncertainties appropriately. A comparative study with the state-of-the-art type-1 fuzzy based image retrieval approaches reveals the effectiveness of the proposed system. Index Terms-Type-2 fuzzy logic, semantic-based image retrieval, soft computing, image processing.