A segmentation method for images compressed by fuzzy transforms

In this paper we describe a segmentation method applied to images which are compressed by using Fuzzy Transforms. The segmentation of the images is realized via the FGFCM (Fast Generalized Fuzzy C-Means) clustering algorithm, which is robust to noise and outliers. The optimal number of clusters is d...

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Bibliographic Details
Published in:Fuzzy sets and systems Vol. 161; no. 1; pp. 56 - 74
Main Authors: Di Martino, Ferdinando, Loia, Vincenzo, Sessa, Salvatore
Format: Journal Article
Language:English
Published: Elsevier B.V 2010
Subjects:
Compressed
FGFCM
Fuzzy
Fuzzy logic
Fuzzy relation
Fuzzy set theory
Fuzzy transform
Image segmentation
Lukasiewicz t-norm
PCAES
PSNR
Segmentation
Similarity
Similarity measure
Transforms
Image segmentation
Lukasiewicz t-norm
PSNR
FGFCM
Similarity measure
Fuzzy relation
PCAES
Fuzzy transform
ISSN:0165-0114, 1872-6801
Online Access:Get full text
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Description
Summary:In this paper we describe a segmentation method applied to images which are compressed by using Fuzzy Transforms. The segmentation of the images is realized via the FGFCM (Fast Generalized Fuzzy C-Means) clustering algorithm, which is robust to noise and outliers. The optimal number of clusters is determined via the PCAES (Partition Coefficient And Exponential Separation) validity index. We use a similarity measure defined via Lukasiewicz t-norm for comparison between the original image and the reconstructed images. The best results are obtained if this similarity measure overcomes a threshold value, experimentally determined from the analysis of the trend of it with respect to the PSNR (Peak Signal to Noise Ratio).
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2009.08.002