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Algorithms for Fast Vector Quantization
 Proc. of DCC '93: Data Compression Conference
, 1993
"... Nearest neighbor searching is an important geometric subproblem in vector quantization. ..."
Abstract

Cited by 80 (11 self)
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Nearest neighbor searching is an important geometric subproblem in vector quantization.
Fast texture synthesis using treestructured vector quantization
, 2000
"... Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given ..."
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Cited by 561 (12 self)
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Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using treestructured vector quantization.
Vector Quantization with Complexity Costs
, 1993
"... Vector quantization is a data compression method where a set of data points is encoded by a reduced set of reference vectors, the codebook. We discuss a vector quantization strategy which jointly optimizes distortion errors and the codebook complexity, thereby, determining the size of the codebook. ..."
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Cited by 63 (20 self)
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Vector quantization is a data compression method where a set of data points is encoded by a reduced set of reference vectors, the codebook. We discuss a vector quantization strategy which jointly optimizes distortion errors and the codebook complexity, thereby, determining the size of the codebook
Multiresolution vector quantization
 IEEE TRANS. INF. THEORY
, 2004
"... Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source descr ..."
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Cited by 45 (4 self)
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description and decode some fraction of that string. Decoding a small portion of the binary string gives a lowresolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms
Soft Learning Vector Quantization
 NEURAL COMPUTATION
, 2002
"... Learning Vector Quantization is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here we take a more principled approach and derive two variants of Learning Vector Quantiz ..."
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Cited by 55 (0 self)
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Learning Vector Quantization is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here we take a more principled approach and derive two variants of Learning Vector
Generalized learning vector quantization
 Hasselmo (Eds.), NIPS
, 1995
"... We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ), " in which reference vectors are updated based on the steepest descent method in order to minimize the cost function. The cost function is determined so that the obtained learning rule satisfies the conver ..."
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Cited by 123 (0 self)
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We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ), " in which reference vectors are updated based on the steepest descent method in order to minimize the cost function. The cost function is determined so that the obtained learning rule satisfies
DistortionLimited Vector Quantization
 in Proc.Data Compression Conf.  DCC ’96
, 1996
"... This paper presents a vector quantization system that limits the maximum distortion introduced to a preselected threshold value. This system uses a recently introduced variation of the L1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected thresho ..."
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Cited by 3 (1 self)
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This paper presents a vector quantization system that limits the maximum distortion introduced to a preselected threshold value. This system uses a recently introduced variation of the L1 distortion measure that attempts to minimize the occurrences of quantization errors above a preselected
Kernelizing Vector Quantization Algorithms
"... Abstract. The kernel trick is a well known approach allowing to implicitly cast a linear method into a nonlinear one by replacing any dot product by a kernel function. However few vector quantization algorithms have been kernelized. Indeed, they usually imply to compute linear transformations (e.g. ..."
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Abstract. The kernel trick is a well known approach allowing to implicitly cast a linear method into a nonlinear one by replacing any dot product by a kernel function. However few vector quantization algorithms have been kernelized. Indeed, they usually imply to compute linear transformations (e
Fractal Dimension and Vector Quantization
, 2004
"... We show that the performance of a vector quantizer for a selfsimilar data set is related to the intrinsic ("fractal") dimension of the data set. We derive a formula for predicting the errorrate, given the fractal dimension and discuss how we can use our result for evaluating the performa ..."
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Cited by 1 (0 self)
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We show that the performance of a vector quantizer for a selfsimilar data set is related to the intrinsic ("fractal") dimension of the data set. We derive a formula for predicting the errorrate, given the fractal dimension and discuss how we can use our result for evaluating
Results 1  10
of
3,989