N.B. Karayiannis and P.I. Pai, "Fuzzy Vector Quantization Algorithms and Their Application in Image Compression," IEEE Trans. Image Processing, Vol. 4, pp. 1193-1201, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....all the neurones. With this general structure, various learning algorithms have been designed and developed such as Kohonen s self organizing feature mapping [10,13,18,33,52,70] competitive learning [1,54,55,65] frequency sensitive competitive learning [1,10] fuzzy competitive learning [11,31,32], general learning [25,49] and distortion equalized fuzzy competitive learning [7] and PVQ (predictive VQ) neural networks [46] Let W (t) be the weight vector of the ith neurone at the tth iteration, the basic competitive learning algorithm can be summarized as follows: z 1 d(x, ....

....other (M 1) neurones. This algorithm can also be classi ed as a variation of Kohonen s selforganizing neural network [33] Around the competitive learning scheme, fuzzy membership functions are introduced to control the transition from soft to crisp decisions during the code book design process [25,31]. The essential idea is that one input vector is assigned to a cluster only to a certain extent rather than either in or out . The fuzzy assignment is useful particularly at earlier training stages which guarantees that all input vectors are included in the formation of a new code book ....

[Article contains additional citation context not shown here]

N.B. Karayiannis, P.I. Pai, Fuzzy vector quantization algorithms and their application in image compression, IEEE Trans. Image Process. 4 (9) (1995) 1193}1201.

....a fuzzy set, while a membership function measures the possibility that each training vector belongs to this set. The fuzzy k means (FKM) algorithm also known as fuzzy ISODATA proposed by Dunn [2] and extended by Bezdek [3] and the fuzzy vector quantization (FVQ) algorithm proposed by Karayiannis [4] will be used for decision level fusion. The fuzzy clustering algorithms will be used to combine results coming from various single modality person authentication algorithms (e.g. from speech, video, still images) The methods provide results accompanied with a degree of quality. The quality ....

....membership functions (0) take very small values and that vector does not practically modify the cluster centers. Thus, the fuzzy clustering algorithms are not seriously affected by the presence of outliers. D. The fzzy vector quantization algorithm The fuzzy vector quantization algorithm (FVQ) [4] is a clustering algorithm based on soft decisions, that leads to crisp decision at the end of the codebook design process. In the initial stages of the algorithm, any training vector may be assigned to the codebook vectors that are included in a hypersphere centered at the vector. The possibility ....

N.B. Karayiannis, "fuzzy vector quantization algorithms and their application in image compression," IEEE Transactions on Image Processing, vol. 4, pp. 1193-1201, September 1995.

....(VQ) and, more generally, unsupervised learning (or clustering) are employed in several elds. Among them, we have speech compression [1] image compression [2] pattern recognition [3] and computer vision [4] Several approaches to clustering exist in literature, both of the fuzzy type [5] [6] and of the hard type [7] 8] Moreover, both of these kinds of algorithms can be further subdivided in c means techniques [7] 5] and competitive learning techniques [8] 9] 6] Some authors [6] 10] say that fuzzy algorithms are less sensitive to initial conditions than hard ones. This is ....

.... [3] and computer vision [4] Several approaches to clustering exist in literature, both of the fuzzy type [5] 6] and of the hard type [7] 8] Moreover, both of these kinds of algorithms can be further subdivided in c means techniques [7] 5] and competitive learning techniques [8] 9] [6]. Some authors [6] 10] say that fuzzy algorithms are less sensitive to initial conditions than hard ones. This is true if we consider the Generalized Lloyd Algorithm (GLA) 7] a hard c means technique, known also as LBG (from the initials of its authors) In [11] and [12] we analyzed the ....

[Article contains additional citation context not shown here]

N.B.Karayiannis and P.-I Pai, \Fuzzy Vector Quantization Algorithms and Their Application in Image Processing," IEEE Transactions on Image Processing, vol. 4, pp. 1193-1201, 1995.

....the performance of GLVQ can drastically deteriorate with a uniform resizing of the data set. Successively, Karayiannis et al. 56] resolved this drawback with a fuzzy modi ed version of GLVQ they called GLVQ F. Karayiannis and Pai underline that fuzzy techniques are less sensitive than others [53, 58]. FALVQ [53] is e ectively less sensitive, but, unfortunately, it depends strongly on the right choice of several parameters required by the algorithm itself. The same thing happens with the Fuzzy c means (FCM) technique [49] Among hard techniques, some based on competitive learning succeed in ....

N.B.Karayiannis and P.-I Pai, \Fuzzy Vector Quantization Algorithms and Their Application in Image Processing," IEEE Transactions on Image Processing, vol. 4, pp. 1193-1201, 1995.

....a fuzzy set, while a membership function measures the possibility that each training vector belongs to this set. The fuzzy k means (FKM) algorithm also known as fuzzy ISODATA proposed by Dunn [2] and extended by Bezdek [3] and the fuzzy vector quantization (FVQ) algorithm proposed by Karayiannis [4] will be used for decision level fusion. The fuzzy clustering algorithms will be used to combine results coming from various single modality person authentication algorithms (e.g. from speech, video, still images) The methods provide results accompanied with a degree of quality. The quality ....

....membership functions (6) take very small values and that vector does not practically modify the cluster centers. Thus, the fuzzy clustering algorithms are not seriously affected by the presence of outliers. D. The fuzzy vector quantization algorithm The fuzzy vector quantization algorithm (FVQ) [4] is a clustering algorithm based on soft decisions, that leads to crisp decision at the end of the codebook design process. In the initial stages of the algorithm, any training vector may be assigned to the codebook vectors that are included in a hypersphere centered at the vector. The possibility ....

N. B. Karayiannis, "Fuzzy vector quantization algorithms and their application in image compression," IEEE Transactions on Image Processing, vol. 4, pp. 1193--1201, September 1995.

....will be taken into account by using fuzzy instead of crisp vectors. The term fuzzy vector will be used in the following, to describe the extension of an n dimensional crisp set C to an n dimensional fuzzy set X defined in an (n 1) dimensional hyperspace, by using a membership function : C # [0, 1] [1] The term fuzzy vector is usually found in the literature, describing the notion of a vector of n 1 dimensional fuzzy numbers. This notion could be appropriate to describe the uncertainty in non correlated data or when different degrees of uncertainty is possible to be given to each signal ....

....taken into account by using fuzzy instead of crisp vectors. The term fuzzy vector will be used in the following, to describe the extension of an n dimensional crisp set C to an n dimensional fuzzy set X defined in an (n 1) dimensional hyperspace, by using a membership function : C # [0, 1] [1]. The term fuzzy vector is usually found in the literature, describing the notion of a vector of n 1 dimensional fuzzy numbers. This notion could be appropriate to describe the uncertainty in non correlated data or when different degrees of uncertainty is possible to be given to each signal ....

[Article contains additional citation context not shown here]

N. B. Karayiannis, P. I. Pai. "Fuzzy Vector Quantization Algorithms and their Application in Image Compression", IEEE Transactions on Image Processing, Vol. 4, No. 9, pp. 1193-1201, 1995.

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N.B. Karayiannis and P.I. Pai, "Fuzzy Vector Quantization Algorithms and Their Application in Image Compression," IEEE Trans. Image Processing, Vol. 4, pp. 1193-1201, 1995.

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