Use of fuzziness measures in layered networks for object extraction: a generalization

Ghosh, Ashish

doi:10.1016/0165-0114(94)00291-e

Cited by 38 publications

(13 citation statements)

References 13 publications

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“…Mentioned uncertainty is often measured by fuzzy entropy, and the explicit fuzzy entropy formulation has also been proposed by numerous researchers [1][2][3][4][5][6][7]. Fuzzy entropy of fuzzy set explains that how much uncertainty fuzzy set has with respect to the corresponding crisp set.…”

Section: Fuzzy Entropy For Fuzzy Setsmentioning

confidence: 99%

“…The fact that entropy of a fuzzy set means a measure of its fuzziness has been established by previous researchers [1][2][3][4][5][6]. Zadeh first proposed fuzzy entropy as a measure of fuzziness; Pal and Pal analyzed classical Shannon information entropy; Kosko considered the relationship between distance measure and fuzzy entropy; Liu proposed axiomatic definitions of entropy, distance measures, and similarity measures and discussed the relationships among these three concepts.…”

Section: Introductionmentioning

confidence: 99%

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Calculation of Data Reliability with Entropy for Fuzzy Sets

Wang¹,

Lee²

2009

International Journal of Fuzzy Logic and Intelligent Systems

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Measuring uncertainty for fuzzy sets has been carried out by calculating fuzzy entropy. Fuzzy entropy of fuzzy set is derived with the help of distance measure. The distance proportional value between the fuzzy set and the corresponding crisp set is designed as the fuzzy entropy. The usefulness is verified by proving the proposed entropy. Generally, fuzzy entropy contains the complementary characteristics that the fuzzy entropies of fuzzy set and complementary fuzzy set have the same entropies. Discrepancy that low fuzzy entropy did not guarantee the data certainty was overcome by modifying fuzzy entropy formulation. Obtained fuzzy entropy is analyzed and discussed through simple example.

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Section: Fuzzy Entropy For Fuzzy Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calculation of Data Reliability with Entropy for Fuzzy Sets

Wang¹,

Lee²

2009

International Journal of Fuzzy Logic and Intelligent Systems

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“…Another definition of fuzzy entropy, given by Pal and Pal (1991) and Ghosh (1995), using exponential gain function is (20) with (21 ) Note that, these entropy measures, first of all, compute the fuzziness related to individual pixel of the image and then make an average over all the pixels to get a quantification of the amount of average ambiguity, the image possesses. Since their computation depends only on the histogram, they may be called global fuzzy entropy.…”

Section: Fuzzy Entropy Of An Imagementioning

confidence: 99%

Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation

Pal

Ghosh

Shankar

2000

International Journal of Remote Sensing

159

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Abstract. Effectiveness of various fuzzy thresholding techniques (based on entropy of fuzzy sets, fuzzy geometrical properties, and fuzzy correlation) is demonstrated on remotely sensed (IRS and SPOT) images. A new quantitative index for image segmentation using the concept of homogeneity within regions is defined. Results are compared with those of probabilistic thresholding, and fuzzy c-means and hard c-means clustering algorithms, both in terms of index value (quantitatively) and structural details (qualitatively). Fuzzy set theoretic algorithms are seen to be superior to their respective non-fuzzy counterparts. Among all the techniques, fuzzy correlation, followed by fuzzy entropy, performed better for extracting the structures. Fuzzy geometry based thresholding algorithms produced a single stable threshold for a wide range of membership variation.

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“…Information quantization represents interesting research theme, in which data vagueness can be illustrated by clear number. Design of fuzzy entropy for calculation of uncertainty has been studied by numerous researchers [1][2][3]. Most of results were concentrated in the designing of fuzzy entropies [1,2], and many parts of them also showed the implicit results of fuzzy entropies [1].…”

Section: Introductionmentioning

confidence: 99%

Mutual Information Analysis with Similarity Measure

Wang¹,

Lee²

2010

International Journal of Fuzzy Logic and Intelligent Systems

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Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure. Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors. This FRIM is analyzed and explained through similarity measure between two fuzzy sets. Furthermore, comparison between two measures is also carried out.

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Use of fuzziness measures in layered networks for object extraction: a generalization

Cited by 38 publications

References 13 publications

Calculation of Data Reliability with Entropy for Fuzzy Sets

Calculation of Data Reliability with Entropy for Fuzzy Sets

Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation

Mutual Information Analysis with Similarity Measure

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