基于迭代主成分分析的哈希算法研究与实现

为了提高高维空间近邻搜索算法的查询性能,本文结合DSH算法和迭代PCA方法的优点提出迭代PCA哈希算法.该算法查询效果良好,充分利用数据集的分布信息、有严格的理论保证.该算法在达到相同精度的条件下较LSH算法和DSH算法查询花费时间少.该算法提供了一种解决近邻搜索问题有效方法.     

基于稀疏自编码的无监督哈希算法

最近邻搜索在大规模图像检索中变得越来越重要。在最近邻搜索中,许多哈希方法因为快速查询和低内存被提出。然而,现有方法在哈希函数构造过程中对数据稀疏结构研究的不足,本文提出了一种无监督的稀疏自编码的图像哈希方法。基于稀疏自编码的图像哈希方法将稀疏构造过程引入哈希函数的学习过程中,即通过利用稀疏自编码器的KL距离对哈希码进行稀疏约束以增强局部保持映射过程中的判别性,同时利用L2范数来哈希编码的量化误差。实验中用两个公共图像检索数据集CIFAR-10和YouTube Faces验证了本文算法相比其他无监督哈希算法的优越性。     

M2LSH:基于LSH的高维数据近似最近邻查找算法

在许多应用中,LSH(Locality Sensitive Hashing)以及各种变体,是解决近似最近邻问题的有效算法之一.虽然这些算法能够很好地处理分布比较均匀的高维数据,但从设计方案来看,都没有针对数据分布不均匀的情况做相应的优化.针对这一问题,本文提出了一种新的基于LSH的解决方案(M2LSH,2 Layers Merging LSH),对于数据分布不均匀的情况依然能得到一个比较好的查询效果.首先,将数据存放到具有计数功能的组合哈希向量表示的哈希桶中,然后通过二次哈希将这些桶号投影到一维空间,在此空间根据各个桶中存放的数据个数合并相邻哈希桶,使得新哈希桶中的数据量能够大致均衡.查询时仅访问有限个哈希桶,就能找到较优结果.本文给出了详细的理论分析,并通过实验验证了M2LSH的性能,不仅能减少访问时间,也可提高结果的正确率.     

一种基于学习的自适应哈希算法研究

通常在一般关系数据库中采用的哈希函数都是针对某一应用而设计的。在具体应用中该函数也许是最优化的．但不能保证该函数适用于其他应用场合。本文提出一种基于枚举的自适应哈希算法并对该算法进行研究。实验表明．该算法能够使数据分布达到最优化，显著地提高数据的存取和查询效率。     

各向同性的迭代量化哈希算法

准确有效的哈希算法是实现海量高维数据近邻检索的关键.迭代量化哈希（Iterative Quantization,ITQ）和各向同性哈希（Isotropic Hash,IsoHash）是两种知名的编码方法.但是ITQ算法对旋转矩阵施加的约束过于单薄,容易导致过拟合;而IsoHash算法缺乏对哈希编码的更新策略,降低了编码质量.针对上述问题,提出了一种各向同性的迭代量化哈希算法.该方法采用迭代的策略,对编码矩阵和旋转矩阵交替更新,并在正交约束的基础上增加各向同性约束来学习最优旋转矩阵,最小化量化误差.在CIFAR-10、22K LabelMe和ANN_GIST_1M基准库上与多种方法进行对比,实验结果表明本文算法在查准率、查全率以及平均准确率均值等指标上均明显优于对比算法.     

无线传感器网络中基于值的kNN查询处理

提出一种基于值的kNN查询处理算法,该算法运用哈希函数将节点的数据映射到一个子区域中存储,采用基于位置路由实现了查询处理,并通过多点存储和可变存储区域减少节点的能量开销.实验结果显示该算法在生命周期和延迟方面都取得较好的效果.     

基于压缩感知的图像哈希算法

在信息安全相关研究中,图像哈希算法是一项热门的内容,通过一串短效的字符、数字序列,对一副图像进行映射,在数字水印、图像检索、图像索引、图像认证等方面,均有着广泛的应用.哈希算法的两个基本性质就是感知鲁棒性、惟一性.传统的密码学哈希算法,一般仅适用于文本数据,需要设计开发新的哈希算法,用于图像等多媒体数据的处理.基于此,本文基于数字图像,提出了基于压缩感知的图像哈希算法,分别以颜色向量角、环形分割为切入点,对图像哈希算法进行了研究.     

基于深度哈希的图像检索技术研究

本文提出的方法基于深度卷积神经网络和哈希算法,该方法对VGG16网络模型进行了改进：模型中全连接层保留FC6和FC7,去掉的FC8用哈希层替换,构建哈希函数获得哈希编码,在损失层对损失函数做了优化计算,微调模型初始化参数。图像检索过程主要包括模型训练和检索图像两个阶段,实验数据集采用CIFAR-10和NUS-WIDE。和其他几种典型的传统哈希算法和深度哈希算法进行对比分析,实验结果表明,本文所提方法能有效提高图像检索性能。     

改进一致性哈希优化存储邮政数据算法的研究

随着电子商务不断发展,邮政快递行业数据日益增多,传统方式对于邮政数据存储的理论与方法都已无法满足需求。基于此情况,使用一致性哈希算法来解决存储系统的横向弹性扩展,结合一致性哈希的虚拟节点与加权轮询算法优化Hadoop平台下分布式文件系统（HDFS）存储策略,实现集群在同构与异构条件下的数据均衡效果。同时介绍集群节点数据转移思想,设计负载因子与系统自检周期,实现了集群动态权重的负载转移,并进行实验验证。实验结果表明,文章提出的改进算法与HDFS、普通一致性哈希相比,在不同条件下集群负载差值均有不同程度的提升,证明了该策略可以有效降低集群节点间负载差值。     

基于Merkle哈希树的无线躯体传感器网络安全认证方案

针对无线躯体传感器网络(WBSN)数据传输的安全性,提出一种融合Merkle哈希树和网络编码的轻量级认证方案。首先,将传感器网络构建成Merkle哈希树结构,只对根节点进行数字签名;然后,在哈希树中选择一个最优层进行网络编码,形成恢复数据包,并将数据包、签名和恢复包发送给接收器;最后,接收器通过密钥对根节点签名进行验证,若存在节点丢失,则根据恢复数据包重建哈希树,从而对数据进行认证。实验结果表明,该方案能够实现对数据的安全认证,且需要较少的网络开销,满足WBSN的性能需求。     

基于加权自学习散列的高维数据最近邻查询算法

因为查询和存储具有高效性,学习型散列逐渐被应用于解决最近邻查询问题.学习型散列将高维数据转化成二进制编码,并使得原始高维空间中越相似的数据对应二进制编码的汉明距离越小.在实际应用中,每次查询都会返回许多与查询点汉明距离相同而编码互不相同的数据.如何对这些数据进行排序是一个难题.提出了一种基于加权自学习散列的近邻查找算法.实验结果表明,算法能够高效地对具有相同汉明距离的不同编码进行重排序,加权排序后查询的F1值约是原来的2倍并优于同系算法,时间开销可比直接计算原始距离进行排序降低一个数量级.     

Effective and efficient indexing in cross-modal hashing-based datasets

To overcome the barrier of storage and computation, the hashing technique has been widely used for nearest neighbor search in multimedia retrieval applications recently. Particularly, cross-modal retrieval that searches across different modalities becomes an active but challenging problem. Although numerous of cross-modal hashing algorithms are proposed to yield compact binary codes, exhaustive search is impractical for large-scale datasets, and Hamming distance computation suffers inaccurate results. In this paper, we propose a novel search method that utilizes a probability-based index scheme over binary hash codes in cross-modal retrieval. The proposed indexing scheme employs a few binary bits from the hash code as the index code. We construct an inverted index table based on the index codes, and train a neural network for ranking and indexing to improve the retrieval accuracy. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated and compared with several state-of-the-art cross-modal hashing methods. Results show the proposed method effectively boosts the performance on search accuracy, computation cost, and memory consumption in these datasets and hashing methods. The source code is available on https://github.com/msarawut/HCI.     

Deep multi-query video retrieval

Video retrieval methods have been developed for a single query. Multi-query video retrieval problem has not been investigated yet. In this study, an efficient and fast multi-query video retrieval framework is developed. Query videos are assumed to be related to more than one semantic. The framework supports an arbitrary number of video queries. The method is built upon using binary video hash codes. As a result, it is fast and requires a lower storage space. Database and query hash codes are generated by a deep hashing method that not only generates hash codes but also predicts query labels when they are chosen outside the database. The retrieval is based on the Pareto front multi-objective optimization method. Re-ranking performed on the retrieved videos by using non-binary deep features increases the retrieval accuracy considerably. Simulations carried out on two multi-label video databases show that the proposed method is efficient and fast in terms of retrieval accuracy and time.     

基于非对称监督深度离散哈希的图像检索

哈希广泛应用于图像检索任务。针对现有深度监督哈希方法的局限性,该文提出了一种新的非对称监督深度离散哈希(ASDDH)方法来保持不同类别之间的语义结构,同时生成二进制码。首先利用深度网络提取图像特征,根据图像的语义标签来揭示每对图像之间的相似性。为了增强二进制码之间的相似性,并保证多标签语义保持,该文设计了一种非对称哈希方法,并利用多标签二进制码映射,使哈希码具有多标签语义信息。此外,引入二进制码的位平衡性对每个位进行平衡,鼓励所有训练样本中的–1和+1的数目近似。在两个常用数据集上的实验结果表明,该方法在图像检索方面的性能优于其他方法。     

Spline regression hashing for fast image search

Techniques for fast image retrieval over large databases have attracted considerable attention due to the rapid growth of web images. One promising way to accelerate image search is to use hashing technologies, which represent images by compact binary codewords. In this way, the similarity between images can be efficiently measured in terms of the Hamming distance between their corresponding binary codes. Although plenty of methods on generating hash codes have been proposed in recent years, there are still two key points that needed to be improved: 1) how to precisely preserve the similarity structure of the original data and 2) how to obtain the hash codes of the previously unseen data. In this paper, we propose our spline regression hashing method, in which both the local and global data similarity structures are exploited. To better capture the local manifold structure, we introduce splines developed in Sobolev space to find the local data mapping function. Furthermore, our framework simultaneously learns the hash codes of the training data and the hash function for the unseen data, which solves the out-of-sample problem. Extensive experiments conducted on real image datasets consisting of over one million images show that our proposed method outperforms the state-of-the-art techniques.     

A novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI)

Recently, techniques that can automatically figure out the incisive information from gigantic visual databases are urging popularity. The existing multi-feature hashing method has achieved good results by fusing multiple features, but in processing these multi-features, fusing multi-features into one feature will cause the feature dimension to be very high, increasing the amount of calculation. On the one hand, it is not easy to discover the internal ties between different features. This paper proposes a novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI) method to learn multiple views in a composite manner. The proposed scheme learns the binary codes of various information sources in a composite manner, and our scheme relies on weighted multiple information sources and improved KNN concept. In particular, here we adopt an adaptive weighing scheme to preserve the similarity and consistency among binary codes. Precisely, we follow the graph modeling theory to construct improved KNN concept, which further helps preserve different statistical properties of individual sources. The important aspect of improved KNN scheme is that we can find the neighbors of a data point by searching its neighbors’ neighbors. During optimization, the sub-problems are solved in parallel which efficiently lowers down the computation cost. The proposed approach shows consistent performance over state-of-the-art (three single-view and eight multi-view approaches) on three broadly followed datasets viz. CIFAR-10, NUS-WIDE and Caltech-256.     

Encoding and Decoding Binary Product Lattices

A binary product lattice is generated from two binary component lattices of lower dimensions by employing the Kronecker product. This work focuses on codes carved from binary product lattices. Defined as such, an intriguing problem is that of effectively mapping independent data sequences onto a selected subset of lattice points. A novel approach is disclosed yielding an explicit connection between source bits and lattice points. Decoding methods typically used for binary product codes do not apply for product lattices. Several alternative decoding approaches are discussed. In particular, a provably bounded-distance decoder is presented. It relies on the fact that a product lattice code point may be regarded as a two-dimensional array whose rows and columns are points in the component lattices. The obtained results are compared with classical lattices known in the art     

Manifold-ranking embedded order preserving hashing for image semantic retrieval

Due to the storage and computational efficiency of hashing technology, it has proven a valuable tool for large scale similarity search. In many cases, the large scale data in real-world lie near some (unknown) low-dimensional and non-linear manifold. Moreover, Manifold Ranking approach can preserve the global topological structure of the data set more effectively than Euclidean Distance-based Ranking approach, which fails to preserve the semantic relevance degree. However, most existing hashing methods ignore the global topological structure of the data set. The key issue is how to incorporate the global topological structure of data set into learning effective hashing function. In this paper, we propose a novel unsupervised hashing approach, namely Manifold-Ranking Embedded Order Preserving Hashing (MREOPH). A manifold ranking loss is introduced to solve the issue of global topological structure preserving. An order preserving loss is introduced to ensure the consistency between manifold ranking and hamming ranking. A hypercubic quantization loss is introduced to learn discrete binary codes. The information theoretic regularization term is taken into consideration for preserving desirable properties of hash codes. Finally, we integrate them in a joint optimization framework for minimizing the information loss in each processing. Experimental results on three datasets for semantic search clearly demonstrate the effectiveness of the proposed method.     

基于模拟退火的二相码选码方法

二相码信号由于实现简单,已经广泛地应用于各种脉冲压缩雷达,然而到目前,高压缩比、低旁瓣二相码信号的优选由于数学处理的困难和计算代价的昂贵一直没有很好解决。本文引入神经元计算的思想,提出了基于模拟退火的二相码选码方法。实验结果表明,这种方法不仅能搜索到最优码,搜索次数大大减少,而且易于做到优选二相码的优化程度与计算代价之间的折衷。