Abstract
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.
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Reyes, C., Durán, M.L., Alonso, T., Rodríguez, P.G., Caro, A. (2008). Behaviour of Texture Features in a CBIR System. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_53
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DOI: https://doi.org/10.1007/978-3-540-87656-4_53
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