Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Image retrieval by color semantics
Multimedia Systems - Special issue on video content based retrieval
Visual information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching and retrieval based on the vocabulary and grammar of color patterns
IEEE Transactions on Image Processing
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
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A texture retrieval system with linguistic descriptions is proposed in this study. Users can pose textural descriptions or visual examples to find the desired texture. A mapping mechanism between low-level statistic features and high-level semantic information is formulated. The proposed system contains three major parts, including texture analysis, fuzzy clustering and similarity computation. For texture-analysis, six Tamura features are extracted from each texture image in the database. For fuzzy clustering, an unsupervised fuzzy clustering algorithm is proposed to generate membership functions with five degrees for each feature. Therefore, degrees of appearance for each feature are interpretcd as five linguistic terms. For similarity computation, a user's query is first translated into a query matrix through the generated membership functions. A similarity metric is proposed to compute the similarity between the query matrix and feature matrix of corresponding texture images. Several empirical tests are given to demonstrate the performance of the proposed system.