Fuzzy entropy: a brief survey

Al-Sharhan, Salah; Karray, Fakhri; Gueaieb, Wail; Basir, Otman

doi:10.1109/fuzz.2001.1008855

Cited by 92 publications

(42 citation statements)

References 18 publications

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“…Theentropy of a point in the fuzzy theory domain can be defined using the concept of membership functions. For a fuzzy set A with membership function µ A , the fuzzy entropy of a point x for |Y | membership functions is computed as follows [248] (here, µ i (x) denotes the degree of membership of point x with respect to class i):…”

Section: Batch Mode Active Learning For Fuzzy Label Problemsmentioning

confidence: 99%

Batch Mode Active Learning for Multimedia Pattern Recognition

Chakraborty

Balasubramanian

Panchanathan

2012

2012 IEEE International Symposium on Multimedia

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The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human la-

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Section: Batch Mode Active Learning For Fuzzy Label Problemsmentioning

confidence: 99%

Batch Mode Active Learning for Multimedia Pattern Recognition

Chakraborty

Balasubramanian

Panchanathan

2012

2012 IEEE International Symposium on Multimedia

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“…According to the information theory, entropy is a measure of the information uncertainty. Fuzzy entropy is defined as a quantity measure of the fuzzy information gained from a fuzzy set or fuzzy system [17]. In an image captured for dimensional measurement, the gray levels are often affected by different factors such as lens magnification, lighting, and noises which may result in vagueness and ambiguity uncertainties.…”

Section: Image Definition Evaluation Based On Fuzzy Entropymentioning

confidence: 99%

“…Fuzzy entropy is the entropy of a fuzzy set, loosely representing the information of uncertainty. Fuzzy set theory has been developed to mimic the powerful capability of human reasoning to design systems that can effectively deal with complex processes [17]. By definition, a fuzzy set is a set containing elements with varying membership degrees.…”

Section: Image Definition Evaluation Based On Fuzzy Entropymentioning

confidence: 99%

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An image auto-focusing algorithm for industrial image measurement

Liu

Yang

2016

EURASIP J. Adv. Signal Process.

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Auto-focusing task, which automatically obtains the best image focus, plays an important role to improve the image definition for the industrial image measurement application. Image-based auto-focusing is one of the widely used methods for this task because of its fast response, convenience, and intelligence. In general, the image-based auto-focusing algorithm often consists of two important steps which are the image definition evaluation and the search strategy. In this paper, we have developed an image auto-focusing algorithm for industrial image measurement. First, we propose a new image definition evaluation method based on the fuzzy entropy, which can reduce the negative effects of noise and variations of light intensity and lens magnification. Second, a combined search method is proposed to combine the multi-scale global search and fine-level curve fitting method, which can avoid the disturbance of the local peaks and obtain the best image focus. The proposed image auto-focusing algorithm has the advantages of high focusing accuracy, high repeatability and stability under the variations of lens magnification, and light intensity index, which make it applicable for the industrial image measurement. Experimental results and comparisons on the practical industrial image measurement system have been presented to show the effectiveness and superiority of the proposed algorithm.

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“…An extension to the image congealing approach that uses fuzzy-entropy (FE) as an objective function is described in Mac Parthalain and Strange (2013) and uses a definition of FE that is derived from the work in Hu et al (2006) and Kosko (1986). Several different definitions for FE have been proposed (Al-Sharhan et al 2001), but this work utilises an approach based on similarity relations, which is described below.…”

Section: Fuzzy-entropy-based Image Congealingmentioning

confidence: 99%

Handwritten Chinese character recognition using fuzzy image alignment

Shen

et al. 2015

Soft Comput

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The task of handwritten Chinese character recognition is one of the most challenging areas of human handwriting classification. The main reason for this is related to the writing system itself which encompasses thousands of characters, coupled with high levels of diversity in personal writing styles and attributes. Much of the existing work for both online and off-line handwritten Chinese character recognition has focused on methods which employ feature extraction and segmentation steps. The preprocessed data from these steps form the basis for the subsequent classification and recognition phases. This paper proposes an approach for handwritten Chinese character recognition and classification using only an image alignment technique and does not require the aforementioned steps. Rather than extracting features from the image, which often means building models from very large training data, the proposed method instead uses the mean image transformations as a basis for model building. Northwestern Polytechnical University, Xi'an 710072, China no subjective tuning of the feature extraction is required. In addition by employing a fuzzy-entropy-based metric, the work also entails improved ability to model different types of uncertainty. The classifier is a simple distance-based nearest neighbour classification system based on template matching. The approach is applied to a publicly available real-world database of handwritten Chinese characters and demonstrates that it can achieve high classification accuracy and is robust in the presence of noise.

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Fuzzy entropy: a brief survey

Cited by 92 publications

References 18 publications

Batch Mode Active Learning for Multimedia Pattern Recognition

Batch Mode Active Learning for Multimedia Pattern Recognition

An image auto-focusing algorithm for industrial image measurement

Handwritten Chinese character recognition using fuzzy image alignment

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