Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of gray value distribution of run lengths for texture analysis
Pattern Recognition Letters
Computer and Robot Vision
Efficient Similarity Search in Feature Spaces with the Q-Tree
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
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Searching and processing in databases of general and non-specific images are highly subjective. The process of texture feature extraction from images produces results of highly theoretical and mathematical character that have little to do with human perception. We present a method to select from low-level texture features, statistics and numerical groupings and to transform them into other high-level features, with visual meaning. We also aim to facilitate their use within CBIR systems. The detailed study of the composition and behaviour of the texture characteristics has enabled us to abstract and use them in an automated manner, similarly to how an observer would do.