Efficient Web-based retrieval of radiographic images using a new multi-scale tree vector quantization in the wavelet domain

2002 ◽  
Author(s):  
Shuyu Yang ◽  
S. Mitra
Keyword(s):  
Vector Quantization ◽  
Wavelet Domain ◽  
Radiographic Images ◽  
Web Based ◽  
Multi Scale

scholarly journals Learning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES

2018 ◽  
Vol 7 (3.20) ◽  
pp. 381
Author(s):  
Eneng Tita Tosida ◽  
Fajar Delli Wihartiko ◽  
Indra Lumesa
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Vector Quantization ◽  
Support System ◽  
Census Data ◽  
Learning Vector Quantization ◽  
Web Based ◽  
Id3 Algorithm ◽  
Small Medium Enterprises ◽  
Medium Enterprises

Implementation of Learning Vector Quantization (LVQ) Algorithm for classification of Indonesia telematics service is designed and created as a classification system to support the decision of grant aid for Small Medium Enterprises (SMEs). Based on the test results, the LVQ algorithm has the best accuracy (93.11%) when compared with ID3 algorithm (64%) and C45 (62%) for telematics data of National Census of Economic (Susenas 2006). The data is still valid and relevant for use in this research because in Indonesia census data is done every 10 years and there is no update of data until now. LVQ implementation results are applied to a web-based decision support system to predict the provision of assistance for Indonesian telematics services SMEs. Unlike the C45 and ID3 algorithms, the LVQ algorithm generates the weight of a neural network where it difficult to know which attributes are most influential for decision making. But in this study LVQ able to show good performance through the analysis of the relevance of existing conditions by comparing it with the weight value produced by the model that are implemented in a web-based decision support system 

scholarly journals Multi-scale vector quantization with reconstruction trees

2020 ◽  
Author(s):  
Enrico Cecini ◽  
Ernesto De Vito ◽  
Lorenzo Rosasco
Keyword(s):  
Differential Geometry ◽  
Decision Trees ◽  
Vector Quantization ◽  
Concentration Of Measure ◽  
Finite Sample ◽  
Multi Scale ◽  
Unknown Distribution ◽  
Coarse To Fine ◽  
Special Case

Abstract We propose and study a multi-scale approach to vector quantization (VQ). We develop an algorithm, dubbed reconstruction trees, inspired by decision trees. Here the objective is parsimonious reconstruction of unsupervised data, rather than classification. Contrasted to more standard VQ methods, such as $k$-means, the proposed approach leverages a family of given partitions, to quickly explore the data in a coarse-to-fine multi-scale fashion. Our main technical contribution is an analysis of the expected distortion achieved by the proposed algorithm, when the data are assumed to be sampled from a fixed unknown distribution. In this context, we derive both asymptotic and finite sample results under suitable regularity assumptions on the distribution. As a special case, we consider the setting where the data generating distribution is supported on a compact Riemannian submanifold. Tools from differential geometry and concentration of measure are useful in our analysis.

scholarly journals Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3610
Author(s):  
Haonan Su ◽  
Cheolkon Jung ◽  
Long Yu
Keyword(s):  
Near Infrared ◽  
Local Contrast ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Infrared Images ◽  
Multi Scale ◽  
Discrepancy Model ◽  
Fused Image ◽  
Contrast Preservation ◽  
Posteriori Estimation

We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.

Image Segmentation Based on Contextual Label Tree for Retrieval

2010 ◽  
Vol 121-122 ◽  
pp. 563-568
Author(s):  
Guo Qiang Yuan ◽  
He Shan Liu
Keyword(s):  
Image Segmentation ◽  
Hidden Markov ◽  
Fine Scale ◽  
Wavelet Domain ◽  
Segmentation Method ◽  
Coarse Scale ◽  
Label Tree ◽  
Multi Scale ◽  
Tree Node ◽  
Important Constituent

Image segmentation is an important constituent portion in image processing and retrieval. Based on the traditional Wavelet-domain Hidden Markov Tree (HMT) Multi-scale Segmentation method, this paper presents a Contextual Label Tree (CLT) method according to the dependency information between image blocks belong to different scales, including the relation from the father node, the neighbor nodes and the neighbor nodes of the father. This method calculates the maximal similarity using context vectors that exit on every tree node and realizes image segmentation from coarse-scale to fine-scale. Experiments show that this method is satisfied with its segmentation performance.

Computational Science and Engineering On-line: an integrated web-based environment for multi-scale modelling of complex reaction systems

2004 ◽  
Vol 102 (4) ◽  
pp. 353-360 ◽  
Author(s):  
Thanh N. Truong ◽  
Tom Cook ◽  
Manohar Nayak ◽  
Chaiwoot Boonyasiriwat ◽  
Le-Thuy T. Tran ◽  
...  
Keyword(s):  
Computational Science ◽  
Complex Reaction ◽  
Science And Engineering ◽  
Web Based ◽  
Reaction Systems ◽  
Multi Scale ◽  
Scale Modelling ◽  
Computational Science And Engineering ◽  
On Line
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