scholarly journals New approach to greedy vector quantization

Bernoulli ◽  
2022 ◽  
Vol 28 (1) ◽  
Author(s):  
Rancy El Nmeir ◽  
Harald Luschgy ◽  
Gilles Pagès
Keyword(s):  
Vector Quantization ◽  
New Approach

Soft Learning Vector Quantization

2003 ◽  
Vol 15 (7) ◽  
pp. 1589-1604 ◽  
Author(s):  
Sambu Seo ◽  
Klaus Obermayer
Keyword(s):  
Vector Quantization ◽  
Gradient Descent ◽  
Distance Measure ◽  
Learning Rule ◽  
Gaussian Mixture ◽  
Classification Performance ◽  
Learning Vector Quantization ◽  
Additional Benefit ◽  
New Approach ◽  
New Methods

Learning vector quantization (LVQ) 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 LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.

WRITER STYLE ADAPTATION IN ONLINE HANDWRITING RECOGNIZERS BY A FUZZY MECHANISM APPROACH: THE ADAPT METHOD

2007 ◽  
Vol 21 (01) ◽  
pp. 99-116 ◽  
Author(s):  
HAROLD MOUCHÈRE ◽  
ERIC ANQUETIL ◽  
NICOLAS RAGOT
Keyword(s):  
Vector Quantization ◽  
Fuzzy Inference ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Intrinsic Properties ◽  
Rate Reduction ◽  
Adaptation Mechanism ◽  
Inference System ◽  
New Approach ◽  
Online Handwriting

This study presents an automatic online adaptation mechanism to the handwriting style of a writer for the recognition of isolated handwritten characters. The classifier we use here is based on a Fuzzy Inference System (FIS) similar to those we have designed for handwriting recognition. In this FIS each premise rule is composed of a fuzzy prototype which represents intrinsic properties of a class. Furthermore, the conclusion part of rules associates a score to the prototype for each class. The adaptation mechanism affects both the conclusions of the rules and the fuzzy prototypes by recentering and reshaping them thanks to a new approach called ADAPT inspired by the Learning Vector Quantization. Thus the FIS is automatically fitted to the handwriting style of the writer that currently uses the system. Our adaptation mechanism is compared with well known adaptation techniques. The tests were based on eight different writers and the results illustrate the benefits of the method in terms of error rate reduction (86% in average). This allows such kind of simple classifiers to achieve up to 98.4% of recognition accuracy on the 26 Latin letters in a writer dependent context.

A Survey: Content Based Image Retrieval using Block Truncation Coding

2018 ◽  
Vol 7 (12) ◽  
pp. 46 ◽  
Author(s):  
Vipul R. Mahajan ◽  
Alka Khade
Keyword(s):  
Vector Quantization ◽  
Image Feature ◽  
Feature Descriptor ◽  
Error Diffusion ◽  
Target Image ◽  
Query Image ◽  
New Approach ◽  
Block Truncation Coding ◽  
Histogram Feature ◽  
Pattern Histogram

A new approach to index color images using the features extracted from the error diffusion Block truncation coding (EDBTC). The EDBTC produces two color quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image feature Descriptor. Herein two features are presented namely, colour histogram feature (CHF),bit Pattern histogram feature (BHF) to measure the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed color quantized and VQ- indexed bitmap image, respectively. The distance calculated from CHF and BHF can be utilized to measure the similarity between two images. A new approach to index colour images using the features extracted from the error diffusion Block truncation coding (EDBTC). The EDBTC produces two colour quantizes and a bitmap Image, which is further, managed using vector quantization (VQ) to create the image feature Descriptor. Herein two features are presented namely, color histogram feature (CHF),bit Pattern histogram feature (BHF) to measure the similarity between a query image and the Target image in database. The CHF and BHF are calculated from the VQ-indexed color quantized and VQ- indexed bitmap image, respectively. The distance calculated from CHF and BHF can be utilized to measure the similarity between two images.

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