It is natural to interpret a monochromatic image of size n × n (pixels) to be a fuzzy relation R whose entries R(x, y) are obtained by normalizing the intensity P(x, y) of each pixel with respect to (for short, w.r.t.) the length L of the scale; that is, R(x, y) = P(x, y)/L. In literature, the usage of fuzzy relation calculus to image processing is well known: e.g., applications to pattern recognition for image restoration and for compression/decompression image procedures. Here we use the greatest eigen fuzzy set (for short, GEFS) A of R w.r.t. the max–min composition and the smallest eigen fuzzy set (for short, SEFS) B of R w.r.t. the min–max composition for applications to problems of image information retrieval. The membership functions of GEFS and SEFS contain values of the assigned fuzzy relation and the pair (A, B) is considered as information granule of R . Indeed, we find the information granules of the original image R and of the retrieved images. Based on these pairs, a similarity measure is also introduced in order to compare R with the retrieved images. The tests are made on the images extracted from ‘View Sphere Database’ (http://www.prima.inrialpes.fr), in which an object is photographed from various directions by using a camera placed on a semisphere whose center is the object itself.

Eigen Fuzzy Sets and Image Information Retrieval

SESSA, SALVATORE;F. DI MARTINO;
2008

Abstract

It is natural to interpret a monochromatic image of size n × n (pixels) to be a fuzzy relation R whose entries R(x, y) are obtained by normalizing the intensity P(x, y) of each pixel with respect to (for short, w.r.t.) the length L of the scale; that is, R(x, y) = P(x, y)/L. In literature, the usage of fuzzy relation calculus to image processing is well known: e.g., applications to pattern recognition for image restoration and for compression/decompression image procedures. Here we use the greatest eigen fuzzy set (for short, GEFS) A of R w.r.t. the max–min composition and the smallest eigen fuzzy set (for short, SEFS) B of R w.r.t. the min–max composition for applications to problems of image information retrieval. The membership functions of GEFS and SEFS contain values of the assigned fuzzy relation and the pair (A, B) is considered as information granule of R . Indeed, we find the information granules of the original image R and of the retrieved images. Based on these pairs, a similarity measure is also introduced in order to compare R with the retrieved images. The tests are made on the images extracted from ‘View Sphere Database’ (http://www.prima.inrialpes.fr), in which an object is photographed from various directions by using a camera placed on a semisphere whose center is the object itself.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/176129
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