Abstract—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 semantic 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.
S. M. Darwish is with the Department of Information Technology,
Institute of Graduate Studies and Research, Alexandria University, 163
Horreya Avenue, El Shatby 21526, P.O. Box 832, Alexandria, Egypt, (tel:
(+203)4295007; e-mail: Saad.darwish@alex-igsr.edu.eg).
Raad A. Ali is with the Department of Computer, Ministry of Education,
Iraq, (e-mail: Raad.ali885@yahoo.com).
[PDF]
Cite:Saad M. Darwish and Raad A. Ali, "Observations on Using Type-2 Fuzzy Logic for Reducing Semantic Gap in Content–Based Image Retrieval System," International Journal of Computer Theory and Engineering vol. 7, no. 1, pp. 1-8, 2015.
