The Kolmogorov–Sinai entropy in the setting of fuzzy sets for image texture analysis and classification. (May 2016)

• Record Type: Journal Article
• Authors: Pham, Tuan D.
• Is Part Of: Pattern recognition. Volume 53(2016:May)
• Page Start: 229
• Page End: 237
• Publication Date: 2016-05
• Subjects: Chaos -- Kolmogorov–Sinai entropy -- Fuzzy sets -- Uncertainty -- Texture analysis -- Image classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.12.012
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: The Kolmogorov–Sinai (K–S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. Sequence probabilities therefore play a central role for the computation of the entropy rate to determine if the dynamical system under study is deterministically non-chaotic, deterministically chaotic, or random. This paper extends the notion of the K–S entropy to measure the entropy rate of imprecise systems using sequence membership grades, in which the underlying deterministic paradigm is replaced with the degree of fuzziness. While constructing sequential probabilities for the calculation of the K–S entropy is difficult in practice, the estimate of the K–S entropy in the setting of fuzzy sets in an image is feasible and can be useful for modeling uncertainty of pixel distributions in images. The fuzzy K–S entropy is illustrated as an effective feature for image analysis and texture classification. Abstract : Highlights: Development of a new method for feature extraction using chaos and fuzzy sets. Modeling Kolmogorov–Sinai (K–S) entropy with fuzzy logic is feasible. Fuzzy K–S entropy is an effective feature for analysis and classification of textural images.