Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

FAST IMAGE ENCODING ALGORITHM BASED ON MEAN-MATCH CORRELATION VECTOR QUANTIZATION

A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3～0.4 dB compared to the full-search VQ.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Image Coding Using Wavelet Transform and EntropyConstrained Vector Quantization with Quadtree Structure Vectors

This paper presents a new wavelet transform image coding method. On the basis of a hierarchical wavelet decomposition of images, entropy constrained vector quantization is employed to encode the wavelet coefficients at all the high frequency bands with

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.

ADCTVQ: A New Method for Image Coding and Compressing

This paper presents a new method for image coding and compressing-ADCTVQ(Adptive Discrete Cosine Transform Vector Quantization). In this method, DCT conforms to visual properties and has an encoding ability which is inferior only to the best transform KLT. Its vector quantization can maintain the minimum quantization distortions and greatly increase the compression ratio. In order to improve compression efficiency, an adaptive strategy of selecting reserved region patterns is applied to preserving the high energy at the same compression ratio. The experiment results show that they are satisfactory at the compression ration ratio if greater than 20.

ROTATED BARNES-WALL LATTICE BASED VECTOR QUANTIZER AND ITS APPLICATION IN IMAGE-SEQUENCE CODING

A new scheme is presented to design a rotated Barnes-Wall lattice based vector quantizer(LVQ). The construction method of the LVQ and its fast quantizing algorithm are described at first. Then gain-shape lattice vector quantizer(GSLVQ) with LVQ as shape quantizer is discussed. Finally the GSLVQ is used in image-sequence coding and good experimental results are obtained.

Combining IFS and VQ in Fractal Image Coding

A novel paradigm for fractal coding selectively corrects the fractal code for selected domain blocks with an image-adaptive VQ codebook. The codebook is generated from the initial uncorrected fractal code and is, therefore, available at the decoder. An efficient trade-off is generated between incremental performance and bit rate.

A New Image Coding Algorithm Based on Self-Organizing Neural Network

The paper deals with a new VQ+DPCM+DCT algorithm based on Self-Organizing Feature Maps(SOFM) algorithm for image coding. In addition. a Frequency sensitive SOFM (FSOFM) has been also devel-oped. Simulation results show that a very good visual quality of the coded image at 0.252 bits/pixel is obtained.

Vector quantization: a review

Vector quantization(VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven types, tree-structured VQ,direct sum VQ, Cartesian product VQ, lattice VQ, classified VQ, feedback VQ, and fuzzy VQ, according to their codebook generation procedures. Over the past decade, quantization-based approximate nearest neighbor(ANN)search has been developing very fast and many methods have emerged for searching images with binary codes in the memory for large-scale datasets. Their most impressive characteristics are the use of multiple codebooks. This leads to the appearance of two kinds of codebook: the linear combination codebook and the joint codebook. This may be a trend for the future. However, these methods are just finding a balance among speed, accuracy, and memory consumption for ANN search, and sometimes one of these three suffers. So, finding a vector quantization method that can strike a balance between speed and accuracy and consume moderately sized memory, is still a problem requiring study.

Fractal Block Coding:A New Method

Fractal image compression is a completely new method to compress images by searching and exploiting the self similarity of the whole image . Fractal Block Coding (FBC) is a practicable fractal coding scheme with annoying slow encoding speed . In this paper, we classify the image blocks by Classified Vector Quantization (CVQ) technique and present an Adaptive Block Truncation Coding (ABTC) scheme to process the midrange blocks in the image. By this method , we reduce the encoding time to one forty fifth comparing to ordinary FBC method with little change in compression ratio and a little decreased coded image quality.

一种进化模拟退火矢量量化图像编码新算法

模拟进化和模拟退火是解决全局优化问题的随机搜索技术 ,它们在工程领域有着广泛的应用 .本文将这两种技术有机结合 ,提出了一种进化模拟退火算法 ,并用其解决图像编码矢量量化码书设计问题 .实验表明 ,算法具有收敛速度快、码书性能佳等优点 .

基于小波变换的有损图像压缩算法研究

文章从实际应用角度出发，全面系统地阐述了小波变换基本理论，提出了一种基于小波变换的有损图像压缩算法。该算法首先利用小波变换对图像进行分解，然后根据各子图像特点对小波系数进行相应的向量量化编码，从而实现图像压缩目的。实验结果表明：该图像压缩算法的使用，可以在图像质量几乎不下降的条件下，极大地提高压缩比，改善恢复图像质量。

一种高效的基于模拟退火的LBG算法

针对传统矢量量化码书设计 L BG算法对初始码书敏感和在迭代过程中容易陷入局部极小的缺陷 ,结合模拟退火算法 ,提出了一种基于模拟退火的 L BG改进算法 ,并给出了退火过程中的扰动因子刻画、扰动策略选取、稳定性判据确定和温度下降策略等细节 .模拟实验结果表明 ,本文所提出的改进算法能够有效地回避对初始码书的敏感 ,同时在搜索性能和图像压缩后还原质量上都得到很好的改善 .

基于人工神经网络的生物组织质谱成像分类与识别方法

生物组织质谱成像技术不仅能够展示组织的生物分子信息,而且能直观地显示分子空间分布,是当今生物质谱的研究热点。如何对生物组织质谱成像的数据进行基于生物分子的有效分类与识别是该领域关注的重要问题,特别对于病变组织与其邻近非病变组织的区分与识别和生物组织功能区域的划分与鉴定具有重要的意义。本研究开发出一种新的分类与识别方法。其流程是,首先进行质谱成像数据预处理,应用无监督的自组织特征映射网络区分组织样品区与非组织区域,提取组织区域的质谱数据,应用有监督的学习向量量化网络对已知类别数据进行学习训练,建立模型;应用模型对未知样品进行识别。应用本方法对6个膀胱癌患者的膀胱癌变组织与邻近非癌变组织的质谱成像数据进行分类与识别,结果显示,癌变组织判错率低于23.38%,而非癌变组织判错率低于9.08%,表现出较高的准确度;对3片邻近的小鼠大脑切片质谱成像数据进行白质与灰质区域划分,将中间的1片用于训练,两边的2片用于验证,结果显示,自组织特征映射网络的分类结果与学习向量量化网络的预测结果不一致率低于4%。本方法基于生物分子的质谱成像组织区域分类与识别,具有较高准确度和操作简便等优点,在临床医学研究领域有大规模的应用潜能。

多小波变换零树图像编码方法研究

研究多小波变换零树图像编码的性能 .在标量零树方面 ,通过对图像多小波系数的重新排列 ,提出了新的多小波系数树的定义 ,并由实验证明它比直接采用单小波零树的组织更为有效 ;在矢量零树方面 ,根据多小波变换处理的是矢量信号这一特点 ,提出了矢量四叉树的数据结构 ,并从最优率失真意义上设计了矢量量化的多小波零树编码方案 ,结果表明 ,在该方案下 ,多小波比优选的

混合量化与基于小波的图像压缩

本文针对基于小波的图像压缩，提出一种新的快速有效的量化方法。该方法将矢量量化的高效率和标量量化的简单性有机地结合起来，称为混合量化（ＨＱ－ＨｙｂｒｉｄＱｕａｎ－ｔｉｚａｔｉｏｎ）。它不需要进行码书训练，不要做乘除法。并用标准测试图像与其它著名方法的压缩结果进行了比较。

一种基于LVQ神经网络与图像处理的火焰识别算法

针对传统火灾探测技术存在的不稳定、误判率高等缺点,通过分析室内火灾图像与常见干扰光源图像的特点,提出一种基于人工神经网络的火焰图像检测技术。对火焰图像的基本特性进行分析,利用火焰图像序列的面积重叠率和中心相对移动率以及颜色等信息,结合实现学习向量量化(LVQ)神经网络融合技术,对视频序列图像中火焰的自动检测。仿真试验结果表明,基于LVQ神经网络的信息融合算法的网络收敛速度较快,有较高的火灾火焰识别准确率。

图像矢量量化──频率敏感自组织特征映射算法

用神经网络实现图像矢量量化是一种非常有效的方法。本文在分析自组织特征映射（ＳＯＦＭ）算法的基础上，提出了一种频率敏感自组织特征映射（ＦＳＯＦＭ）算法，并对网络学习训练参数的优化进行了探讨。实验表明，ＦＳＯＦＭ算法优于ＳＯＦＭ算法。