Handbook of pattern recognition & computer vision
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Communications of the ACM
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
Content-based image retrieval using a composite color-shape approach
Information Processing and Management: an International Journal
Visual information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Retrieval of Visual Media in Multimedia Systems
Representation and Retrieval of Visual Media in Multimedia Systems
Computer Vision
Color image retrieval based on hidden Markov models
IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Multimedia Tools and Applications
Fuzzy Sets for Image Texture Modelling Based on Human Distinguishability of Coarseness
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
A vertical search engine based on visual and textual features
Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
| Hi-index | 0.10 |
In this study, we propose a fuzzy logic CBIR (content-based image retrieval) system for finding textures. In our CBIR system, a user can submit textual descriptions and/or visual examples to find the desired textures. After the initial search, the user can give relevant and/or irrelevant examples to refine the query and improve the retrieval efficiency. Contributions of this study are fourfold. (1) Our CBIR system maps low-level statistical features to high-level textual concepts; it bridges the semantic gap between these two levels. (2) Our CBIR system characterizes texture properties of these two levels; and further, it achieves high-level texture manipulations through textual concepts. (3) Our CBIR system models the human perception subjectivity via relevance feedbacks to perform more accurate retrieval. (4) Our CBIR system provides intuitive and simple methods of similarity definitions and computations. Experimental results reveal our CBIR system is indeed effective. The retrieved images are perceptually satisfactory, and the retrieval time is very short.