应用多索引加法量化编码的近邻检索算法

目的 海量图像检索技术是计算机视觉领域研究热点之一,一个基本的思路是对数据库中所有图像提取特征,然后定义特征相似性度量,进行近邻检索。海量图像检索技术,关键的是设计满足存储需求和效率的近邻检索算法。为了提高图像视觉特征的近似表示精度和降低图像视觉特征的存储空间需求,提出了一种多索引加法量化方法。方法 由于线性搜索算法复杂度高,而且为了满足检索的实时性,需把图像描述符存储在内存中,不能满足大规模检索系统的需求。基于非线性检索的优越性,本文对非穷尽搜索的多索引结构和量化编码进行了探索新研究。利用多索引结构将原始数据空间划分成多个子空间,把每个子空间数据项分配到不同的倒排列表中,然后使用压缩编码的加法量化方法编码倒排列表中的残差数据项,进一步减少对原始空间的量化损失。在近邻检索时采用非穷尽搜索的策略,只在少数倒排列表中检索近邻项,可以大大减少检索时间成本,而且检索过程中不用存储原始数据,只需存储数据集中每个数据项在加法量化码书中的码字索引,大大减少内存消耗。结果 为了验证算法的有效性,在3个数据集SIFT、GIST、MNIST上进行测试,召回率相比近几年算法提升4%~15%,平均查准率提高12%左右,检索时间与最快的算法持平。结论 本文提出的多索引加法量化编码算法,有效改善了图像视觉特征的近似表示精度和存储空间需求,并提升了在大规模数据集的检索准确率和召回率。本文算法主要针对特征进行近邻检索,适用于海量图像以及其他多媒体数据的近邻检索。     

等和值块扩展最近邻矢量量化码字搜索算法

论文提出一种等和值块扩展最近邻矢量量化码字搜索算法。该算法将码书按和值大小排序分块,并将每一块中间或中间附近的码字的和值作为本码书块的特征和值。编码时,查找与输入矢量和值距离最近的码书块并作为初始匹配码书块。然后在该码书块附近上下扩展搜索相邻码书块中距输入矢量最近的码字。该算法具有无复杂运算的特点,易于VLSI技术实现。仿真结果表明,该算法是一种有效的码字搜索算法。     

一种新颖的基于哈德码变换的码字搜索算法

针对图像矢量量化编码的复杂性,提出了一种新颖的快速最近邻码字搜索算法。该算法首先计算出每个码字和输入矢量的哈德码变换,然后为输入矢量选取范数距离最近的初始匹配码字,利用多控制点的三角不等式和两条有效的码字排除准则,把不匹配的码字排除,最后选取与输入矢量最匹配的码字。实验结果表明,新算法相比于其他算法,在保证编码质量的前提下,码字搜索时间和计算量均有了明显降低。     

Hadamard transform based fast codeword search algorithm for high-dimensional VQ encoding

An efficient nearest neighbor codeword search algorithm for vector quantization based on the Hadamard transform is presented in this paper. Four elimination criteria are derived from two important inequalities based on three characteristic values in the Hadamard transform domain. Before the encoding process, the Hadamard transform is performed on all the codewords in the codebook and then the transformed codewords are sorted in the ascending order of their first elements. During the encoding process, firstly the Hadamard transform is applied to the input vector and its characteristic values are calculated; secondly, the codeword search is initialized with the codeword whose Hadamard-transformed first element is nearest to that of the input vector; and finally the closest codeword is found by an up-and-down search procedure using the four elimination criteria. Experimental results demonstrate that the proposed algorithm is much more efficient than the most existing nearest neighbor codeword search algorithms in the case of problems of high dimensionality.     

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.

     

矢量量化的初始码书算法

矢量量化的初始码书设计是很重要的,影响或决定着其后码书形成算法的迭代次数和最终的码书质量。针对原有的初始码书算法在性能上随机性强与信源匹配程度不高的问题,提出一种对于训练矢量实施基于分量的和值排序,然后做分离平均的初始码书形成算法。算法使用了矢量的特征量,脱离了对于图像结构因数的依赖,能产生鲁棒性较好的初始码书。实验证明了该方法的有效性,与LBG算法结合可进一步提高码书质量。     

A novel approach for fast codebook re-quantization

In this paper, we present a fast codebook re-quantization algorithm (FCRA) using codewords of a codebook being re-quantized as the training vectors to generate the re-quantized codebook. Our method is different from the available approach, which uses the original training set to generate a re-quantized codebook. Compared to the traditional approach, our method can reduce the computing time dramatically, since the number of codewords of a codebook being re-quantized is usually much smaller than the number of original training vectors. Our method first classifies codewords of a re-quantized codebook into static and active groups. This approach uses the information of codeword displacements between successive partitions to reject impossible candidates in the partition process of codebook re-quantization. By implementing a fast search algorithm used for vector quantization encoding (MFAUPI) in the partition step of FCRA, the computational complexity of codebook re-quantization can be further reduced significantly. Using MFAUPI, the computing time of FCRA can be reduced by a factor of 1.55–3.78. Compared with the available approach OARC (optimization algorithm for re-quantization codebook), our proposed method can reduce the codebook re-quantization time by a factor of about 8005 using a training set of six real images. This reduction factor is increased when the re-quantized codebook size and/or training set size are increased. It is noted that our proposed algorithm can generate the same re-quantized codebook as that produced by the OARC.     

A new algorithm for Kohonen layer learning with application topower system stability analysis

In certain classification problems, input patterns are not distributed in a clustering manner but distributed uniformly in an input space and there exist certain critical hyperplanes called decision boundaries. Since learning vector quantization (LVQ) classifies an input vector based on the nearest neighbor, the codebook vectors away from the decision boundaries are redundant. This paper presents an alternative algorithm called boundary search algorithm (BSA) for the purpose of solving this redundancy problem. The BSA finds a fixed number of codebook vectors near decision boundaries by selecting appropriate training vectors. It is found to be more efficient compared with LVQ and its validity is demonstrated with satisfaction in the transient stability analysis of a power system.     

基于簇内乘积量化的最近邻检索方法

本文针对大规模高维数据近邻检索中的瓶颈问题,提出基于向量量化的一种检索方法—簇内乘积量化树方法.该方法运用向量量化和乘积量化的多层树状结构高效表征大规模高维数据集,与现有方法相比降低了索引表空桶率;其次提出基于贪心队列的近邻簇筛选方法减小了计算复杂度,加快了近邻检索速度;最后提出面量化方法用于近似计算候选数据集向量与查询向量间的距离,与点量化和线量化方法相比量化误差更小,提高了近邻查询准确率.本文提出的簇内乘积量化树算法在算子Sift和Gist描述的大规模高维数据集上与乘积量化树技术相比,首次召回准确率提高了57.7%,索引表空桶率降低幅度在50%以上,与局部优化乘积量化技术相比,查全率高达97%,而查询时间却仅需原来的1/9.实验结果表明本文提出的基于簇内乘积量化的近邻方法提升了近邻检索性能,为大规模高维数据集近邻检索提供了理论支持.     

基于自组织特征映射神经网络的矢量量化

近年来,许多学者已经成功地将Ｋｏｈｏｎｅｎ的自组织特征映射（ＳＯＦＭ）神经网络应用于矢量量化（ＶＱ）图象压缩编码,相对于传统的ＫＬＢＧ算法,基于的ＳＯＦＭ算法的两个主要缺点是计算量大和生成的码书性能较差因此为了改善码书性能,对基本的ＳＯＦＭ算法的权值调整方法作了一些改进,同时为了降低计算量,又在决定获得胜神经元的过程中,采用快速搜索算法,在将改进的算法用于矢量量化码书设计后,并把生成的码书用于图象     

PDVQ图像编码系统的算法研究

PDVQ图像编码系统首先将码书进行方向性分类,把每类方向性码书中的码字按码字和值进行升序排列,并根据EBNNS算法将码书分块。编码时,先根据输入图像块的相关性进行PDVQ编码,然后分析输入图像块的方向性来选择相应的分类子码书,在该子码书中根据输入图像块的和值确定码字搜索范围,最后在确定的搜索范围内搜索最匹配码字。仿真结果表明,该系统集合了动态图像块划分（PDVQ）、基于方向性分类编码和等和值块扩展最近邻码字搜索（EBNNS）三种算法的优点,在保证重建图像质量前提下,缩短了编码时间,并提高了压缩比。     

An improved search algorithm for vector quantization using mean pyramid structure

Vector quantization (VQ) is a well-known data compression technique. In the codebook design phase as well as the encoding phase, given a block represented as a vector, searching the closest codeword in the codebook is a time-consuming task. Based on the mean pyramid structure and the range search approach, an improved search algorithm for VQ is presented in this paper. Conceptually, the proposed algorithm has the bandpass filter effect. Each time, using the derived formula, the search range becomes narrower due to the elimination of some portion of the previous search range. This reduces search times and improves the previous result by Lee and Chen (A fast search algorithm for vector quantization using mean pyramids of codewords. IEEE Trans. Commun. 43(2/3/4), (1995) 1697–1702). Some experimental results demonstrate the computational advantage of the proposed algorithm.     

基于KD-Tree搜索和SURF特征的图像匹配算法研究

针对图像匹配时进行特征检测和匹配的搜索时间长的问题,文章研究了基于KD-Tree搜索和SURF特征的图像匹配算法。该算法首先提取得到图像的SURF特征并生成特征描述向量,然后为这些特征描述向量建立KD-Tree索引,最后通过计算每个特征点的与其距离最近的若干个KD-Tree上的最近邻点,完成特征匹配工作。实验结果表明,与SIFT算法相比,SURF算法进行特征检测的速度要快2～3倍;与全局最近邻搜索相比,基于KD-Tree索引的近似最近邻搜索大大减少了计算量,较大地提高了SURF算法的匹配速度。     

一种快速的矢量量化编码

研究了一种基于均方误差（MSE）测度的矢量量化快速编码算法。算法利用小波变换的特点,合理地构造矢量,便于非线性插补矢量量化技术的使用,也使部分失真排除法的效率大大提高。使用矢量的二范数和距离测度关系的码字排除方法,再结合非线性插补矢量量化技术和部分失真排除法,在搜索编码过程中,有效排除部分候选码字。实验结果表明,相对于穷尽搜索方法,计算量有明显降低,计算时间显著减少。     

基于概率分布估计算法的进化规划码书设计算法

提出一种以最近邻划分变异为搜索策略,并以EP(进化规划)与EDA(概率密度估计算法)相结合的混合进化方法作为搜索引擎的新型码书设计算法.在最近邻划分上,引入最近邻划分控制因子作为进化算法的染色体表示,实现最近邻划分变异,从而改变质心运动轨迹.染色体与矢量同维,编码空间相对较小,并且进化操作易于控制和实现.在混合进化方法中,EDA为EP提供了最优个体的搜索方向,加速了算法的收敛速度.实验结果表明该方法是能有效提高码书性能的一种优化方法.     

A novel encoding algorithm for vector quantization using transformed codebook

In this paper, a novel encoding algorithm for vector quantization is presented. Our method uses a set of transformed codewords and partial distortion rejection to determine the reproduction vector of an input vector. Experimental results show that our algorithm is superior to other methods in terms of the computing time and number of distance calculations. Compared with available approaches, our method can reduce the computing time and number of distance calculations significantly. Compared with the available best method of reducing the number of distance computations, our approach can reduce the number of distance calculations by 32.3-67.1%. Compared with the best encoding algorithm for vector quantization, our method can also further reduce the computing time by 19.7-23.9%. The performance of our method is better when a larger codebook is used and is weakly correlated to codebook size.     

Minimum cross-entropy pattern classification and cluster analysis

This paper considers the problem of classifying an input vector of measurements by a nearest neighbor rule applied to a fixed set of vectors. The fixed vectors are sometimes called characteristic feature vectors, codewords, cluster centers, models, reproductions, etc. The nearest neighbor rule considered uses a non-Euclidean information-theoretic distortion measure that is not a metric, but that nevertheless leads to a classification method that is optimal in a well-defined sense and is also computationally attractive. Furthermore, the distortion measure results in a simple method of computing cluster centroids. Our approach is based on the minimization of cross-entropy (also called discrimination information, directed divergence, K-L number), and can be viewed as a refinement of a general classification method due to Kullback. The refinement exploits special properties of cross-entropy that hold when the probability densities involved happen to be minimum cross-entropy densities. The approach is a generalization of a recently developed speech coding technique called speech coding by vector quantization.     

Novel full-search schemes for speeding up image coding using vector quantization

The routine for finding the closest codeword in the encoding phase of vector quantization (VQ) is high computational complexity and time consuming, especially when the codewords deal with the high-dimensional vectors. In this paper, we propose three newly developed schemes for speeding up the encoding phase of VQ. The proposed schemes can easily filter out many impossible codewords such that the search domain is reduced. The experimental results show that the computational time of our proposed schemes can save more than 41–52% computational time in a full search scheme. Furthermore, our schemes only require fewer than 84% of the computational time required in recently proposed alternative.