Open AccessSpatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification
Author(s) -
Tuan D. Pham
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0105075
Subject(s) - vagueness , computer science , artificial intelligence , pattern recognition (psychology) , spatial analysis , geostatistics , data mining , feature (linguistics) , fuzzy logic , probability distribution , contextual image classification , feature extraction , machine learning , image (mathematics) , mathematics , spatial variability , statistics , linguistics , philosophy
The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.