基于领域本体的信息智能检索方法的初探

在web信息检索领域,提高检索系统的查全率和查准率是一个重要并且一直在发展的问题。语义相似度计算是基于卒体的检索技术的一个关键问题。所以需要一种基于本体的检索技术来提高效率。本文对已有语义相似度计算方法进行总结并改进,最后对其进行了分析。     

词语相似度算法的分析与改进

对现有的词语相似度算法进行分析,提出一种基于知网,面向语义、可扩展的词语相似度计算方法,通过对实验结果进行分析,所提出的词语语义相似度计算方法比以前的方法更好,在计算词语相似度时,准确率更高。     

基于多层次视觉语义特征融合的图像检索算法

目的为了解决低层特征与中层语义属性间出现的语义鸿沟,以及在将低层特征转化为语义属性的过程中易丢失信息,从而会降低检索精度等问题,设计一种多层次视觉语义特征融合的图像检索算法。方法首先分别提取图像的3种中层特征(深度卷积神经网络(DCNN)特征、Fisher向量、稀疏编码空间金字塔匹配特征(SCSPM));其次,为了对3种特征进行有效融合,定义一种基于图的半监督学习模型,将提取的3个中层特征进行融合,形成一个多层次视觉语义特征,有效结合3种不同中层特征的互补信息,提高图像特征描述,从而降低检索算法中的语义鸿沟;最后,引入具有视觉特性与语义统一的距离函数,根据提取的多层次视觉语义特征来计算查询图像和训练图像的相似度量,完成图像检索任务。结果实验结果表明,与当前检索方法对比,文中算法具有更高的检索精度与效率。结论所提算法具有良好的检索准确度,在医疗、包装商标等领域具有一定的参考价值。     

基于知识图谱和犹豫模糊理论的复杂产品设计知识检索系统

针对复杂产品设计知识检索系统的检索精度低、人员使用要求高、匹配知识粒度大等问题,本文开发了一种基于知识图谱(KG)和模糊相似度的设计知识检索系统。结合设计知识需求及历史设计数据,开发知识表示模型以建立设计知识网络;基于知识图谱的三元组存储形式,构建设计知识检索框架;设计基于犹豫模糊理论的句子检索算法,从词和句子层面融合句子的特征属性,并给出不同属性的隶属度计算公式,对比验证了该算法的平均准确率优于其他相关算法。在上述方法和技术的基础上,以柴油发动机设计知识为例开发设计知识检索系统,通过两类设计知识检索实例,验证了所开发系统的有效性和实用性。     

基于案例的产品设计系统中程序型知识检索研究及实现

基于Internet的计算机辅助工业设计系统,基于案例的计算机辅助工业设计系统研究基础上,结合Case-based Reasoning CBR技术,采用语义隶属度分析的方法,提出了基于设计问题的程序型知识获取模型以及基于相似度计算的程序型知识检索算法,为计算机辅助工业设计提供了一个新的思维方式,并在CBID系统构建中得到实践与验证.     

DDIS：一种新的支持快速相似检索的高维索引结构

基于内容的图像检索是一种典型的相似检索问题，在海量高维的数据集中进行相似匹配，一般认为距离计算费用很高。因此，需要建立有效的索引结构，以减少每个查询中的距离计算次数。为此，针对K最近邻查询（KNN）算法．提出了一种新的高维空间数据索引方法——层次距离索引（DepthDistanceIn—dexStructure，DDIS）。详细讨论了这种索引结构的建立与检索等问题，并给出了相应的算法，实验结果显示了这种索引技术的有效性。     

基于颜色和形状的图像检索

提出一种新的基于颜色和形状的图像检索方法.将彩色图像转换到HSV空间将彩色图像转换到HSV空间,同时将检索图像分块,分别计算每个子块图像的颜色信息熵和中心矩;同时利用Harris算法提取检索图像的兴趣点,计算兴趣点环形空间分布的颜色信息熵和中心矩,以此表征图像的形状特征.针对颜色和形状特征进行高斯归一化,分别计算图像颜色和形状的相似度.最后,利用颜色和形状相似度的加权和进行图像检索.试验结果表明本文算法比CCSI算法和SCH算法具有较高的检索率.     

相似度距离耦合角度径向变换的图像配准算法

目的为了解决当前图像配准算法因利用l1距离或l2距离相似度测量手段来完成图像特征点匹配,使其忽略了相位信息,难以有效消除高斯噪声的影响,使其配准精度与效率不佳不足的问题。方法提出最优相似度距离耦合角度径向变换的抗噪图像配准算法。首先引入角度径向变换,以降低算法复杂度,快速提取图像的特征点。然后联合图像的幅度和相位信息,基于欧式距离测度,定义最优相似度距离测量模型,通过求解其全局最小值,对特征点完成匹配,提高算法的抗噪性能。最后将图像分割为内点与外点,择取6个内点,通过计算其变换矩的几何配准误差,改进随机样本一致策略,对匹配进行提纯,消除误配。结果仿真实验结果显示,与当前基于l1距离或l2距离相似度测量的图像配准技术相比,该算法具有更强的抗高斯噪声性能和更高的匹配精度,且算法时耗最短。结论所提算法能够精确完成图像特征配准。     

基于网络表示学习的本体语义挖掘与功能语义检索方法

为充分利用本体概念之间隐含的语义关系,以支持产品功能创新设计过程,提出了一种基于网络表示学习的本体语义挖掘与功能语义检索方法。首先,基于本体中确定的语义关系,利用网络表示学习挖掘隐含的语义关系;然后,基于语义类比的向量运算,建立本体概念之间潜在的功能语义关系,并对功能语义向量进行表达;最后,通过功能语义向量的相似度计算实现由用户功能需求向跨领域功能性设计资料的扩展,并建立相应的设计资料检索方法和流程。产品设计示例表明,所提出的本体语义挖掘与功能语义检索方法有利于从产品功能角度获取跨领域设计知识,可为设计人员提供更多的灵感。     

基于RBF神经网络集成的三维模型分类和检索

针对单个神经网络难以对复杂的三维模型特征空间有足够的优化能力和泛化能力的问题,用Boosting方法变种和基于粒子群训练的RBF神经网络,形成特征空间对应的多个神经网络,然后将神经网络集成,给出三维模型的分类信息。在三维模型检索时,将神经网络集成输出的分类信息和特征空间上的距离信息进行加权计算,得到三维模型之间的相似度。实验结果表明,基于RBF神经网络集成的分类方法能有效提高三维模型的分类准确率;同时,考虑特征空间上模型间的距离和语义分类层次上模型间的距离,能够大大提高三维模型的检索精度。     

Distance measures for medical image retrieval

Imaging has occupied a huge role in the management of patients, whether hospitalized or not. Depending on the patient's clinical problem, a variety of imaging modalities were available for use. Radiology is the branch of medical science dealing with medical imaging. It may use X‐ray machines or other such radiation devices. It also uses techniques that do not involve radiation, such as magnetic resonance imaging (MRI) and ultrasound (US). Commonly used imaging modalities include plain radiography, computed tomography (CT), MRI, US, and nuclear imaging techniques. Each of these modalities has strengths and limitations which dictates its use in diagnosis. The usage of modality for a particular problem must be reviewed with emphasis on method of generating an image with costs, strengths and weaknesses, and associated risks. The reason for image retrieval is due to increase in acquisition of images. Physicians and radiologists feel better while using retrieval techniques for faster remedy in surgery and medicine due to the following reasons: giving details to the patients, searching the present and past records from the larger databases, and giving solutions to them in a faster and more accurate way. Similarity measures are one of the techniques that help us in retrieval of medical images. Similarity measures also termed as distance metrics, which plays an important role in CBIR and CBMIR. They calculate the visual similarities between the query image and images in the database which were ranked by their similarities with the query image. Different similarity measures have different effects in an image retrieval system significantly. So, it is important to find the best distance metrics for CBIR system. In this article, various distance methods were used and then they are compared for effective medical image retrieval. A double‐step approach is followed for effective retrieval. This article describes some easily computable distance measures for medical image retrieval using measures such as probability, mean, standard deviation, skew, energy, and entropy. The distance measures used are Euclidean, Manhattan, Mahalanobis, Canberra, Bray‐Curtis, squared chord, and Squared chi‐squared. Two kind of decision rules precision and accuracy were used for measuring retrieval. A dataset is created using various imaging modalities like CT, MRI, and US images. From the final results, it is very clear that each distance metric with each measures shows different results in retrieval of medical images. It is found that the distance metrics with all the measures shows different precision and recall value calculated from their retrieved medical images. The best retrieval results for Euclidean distance metric is only with probability measure showing 75% of precision and 30% of recall when comparing with other measures. The best retrieval results for Manhattan distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Mahalanobis distance metric is only with probability as a measure giving 75% of precision and 30% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Canberra distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for Bray‐Curtis distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for squared‐chord distance metric is only with mean as a measure giving 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. The best retrieval results for squared chi‐chord distance metric is only with mean as a measure showing 50% of precision and 20% of recall when compared its performance with other measures in the retrieval of medical images. These results indicate that these easily computable similarity distance measures have a wide variety of medical image retrieval applications. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 9–21, 2013     

基于字典统计耦合归一化多重距离的图像检索算法

目的针对当前彩色图像检索技术容易受到色彩的干扰,使其鲁棒性不强等不足,提出一种字典统计耦合归一化多重距离的彩色图像检索算法。方法首先将图像量化并转换成一维信号,然后引入字典统计,将一维信号进行字典编码,并计算编码后的图像多样值,在归一化字典距离的基础上嵌入字典编码图像的多样值,从而定义归一化多重距离(NMD)的相似度量准则,利用NMD对查询图像与数据库图像的多样值进行比较与识别,搜索出与查询图像具有相同特征的最相似图像,完成目标检索。结果在COREL数据库的实验结果表明,相对于当前常用的检索技术,该检索算法具有更高的查准率和查全率,可对彩色图像完成精确检索,有效减低了色彩对检索性能的干扰。结论文中算法具有较好的检索精度,能够较好地用于医疗、商标等领域的目标检索。     

Shape,Color and Texture Based CBIR System Using Fuzzy Logic Classifier

The perfect image retrieval and retrieval time are the two major challenges in CBIR systems. To improve the retrieval accuracy, the whole database is searched based on many image characteristics such as color, shape, texture and edge information which leads to more time consumption. This paper presents a new fuzzy based CBIR method, which utilizes colour, shape and texture attributes of the image. Fuzzy rule based system is developed by combining color, shape, and texture feature for enhanced image recovery. In this approach, DWT is used to pull out the texture characteristics and the region based moment invariant is utilized to pull out the shape features of an image. Color similarity and texture attributes are extorted using customized Color Difference Histogram (CDH). The performance evaluation based on precision and BEP measures reveals the superiority of the proposed method over renowned obtainable approaches.     

基于WordNet和HowNet建设双语语义词典

针对当前语义信息往往需要手工获取的现状,探讨了利用英语义类词典WordNet和汉语概念词典HowNet资源,为双语机读词典ECDic2K的英语词条添加概念和语义类的自动方法,以改善手工添加语义类带来的低效率和不一致性等问题。实验表明,该方法向英语双汉语机读词典ECDic2K的35848词条译文项自动添加语义信息,而改造的的双语语义词典为单语语义分析中的成熟技术用于跨语言研究提供了信息,这在机器翻译和跨语言检索等自然语言领域具有很高的实用价值。     

基于泽尼克矩耦合颜色空间加权度量的商标检索算法

目的 针对商标检索系统中利用单一特征进行识别和度量时,往往难以充分表征商标特征,易出现检索精度和鲁棒性不高等问题,文中拟设计一种泽尼克（Zernike）矩耦合颜色空间加权度量的商标检索方案。方法 首先,利用Zernike矩作为商标的形状描述符,充分描述商标的形状信息。随后,利用颜色空间来描述图像中像素空间信息的颜色分布特征。然后,分别将输入商标的Zernike矩特征、颜色空间特征与存储在数据库中的特征进行匹配,以计算Zernike矩特征的加权Euclidean距离与颜色空间度量。最后,联合颜色空间度量与Euclidean距离,综合考虑形状与颜色特征,形成新的距离测量规则,输出与查询商标相似的商标。结果 实验数据表明,与当前商标检索算法相比较,所提算法具有更高的检索准确率与鲁棒性,表现出更为理想的Precision-Recall以及平均准确率（Mean Average Precision, MAP）。结论 所提算法返回的图像与查询图像相似度较高,在商标注册、侵权保护等方面中具有一定的参考价值。     

OSIS: remote sensing code for estimating aerosol optical properties in urban areas from very high spatial resolution images

The achievement of new satellite or airborne remote sensing instruments enables the more precise study of cities with metric spatial resolutions. For studies such as the radiative characterization of urban features, knowledge of the atmosphere and particularly of aerosols is required to perform first an atmospheric compensation of the remote sensing images. However, to our knowledge, no efficient aerosol characterization technique adapted both to urban areas and to very high spatial resolution images has yet been developed. The goal of this paper is so to present a new code to characterize aerosol optical properties, OSIS, adapted to urban remote sensing images of metric spatial resolution acquired in the visible and near-IR spectral domains. First, a new aerosol characterization method based on the observation of shadow/sun transitions is presented, offering the advantage to avoid the assessment of target reflectances. Its principle and the modeling of the signal used to solve the retrieval equation are then detailed. Finally, a sensitivity study of OSIS from synthetic images simulated by the radiative transfer code AMARTIS v2 is also presented. This study has shown an intrinsic precision of this tool of Δτ(a)=0.1.τ(a) ± (0.02 + 0.4.τ(a)) for retrieval of aerosol optical thicknesses. This study shows that OSIS is a powerful tool for aerosol characterization that has a precision similar to satellite products for the aerosol optical thicknesses retrieval and that can be applied to every very high spatial resolution instrument, multispectral or hyperspectral, airborne or satellite.     

基于深度学习耦合稀疏语义度量的商标检索算法

目的针对当前商标图像检索中的语义鸿沟问题,提出一种深度学习耦合稀疏语义度量的商标图像检索方案,有效抑制噪声干扰,降低冗余特征维数。方法首先,根据由卷积与池化组成的无监督学习机制,对输入商标图像进行多层特征提取,输出一维特征向量。随后,通过L2-支持向量机(L2-SVM)进行分类,利用特征向量进行训练,获得多级联特征。然后,根据商标图像的多级联特征和用户标签信息的异构数据结构,设计一种稀疏语义度量方法进行相似检索,减少语义鸿沟。此外,引入一种混合范数作为相似度量的稀疏约束,以抑制原始输入空间中的冗余特征维数和噪声,优化检索结果。结果实验表明,与当前流行的商标检索方案相比,所提算法具有更高的检索精度,其输出的结果中仅有1幅无关图像。结论该方案具有较高的检索精度和较强的鲁棒性,在商标检测、商标保护等方面中具有良好的应用价值。     

基于双级特征提取与度量的图像检索算法

目的 针对单一低层特征在语义属性中的信息易丢失,导致其对图像描述能力不强,使其检索精度不佳的问题,结合颜色矩（CM）、角径向变换描述符（ART）和边缘直方图（EH）等3种特征,定义一种双级特征提取与度量的图像检索方案。方法 首先,将图像转换为HSV色彩空间,并将其分割为若干个非重叠子图像,通通过计算每个子图像的均值、标准差和偏斜度来表征CM;再利用Euclidean距离,对查询图像和数据库图像的CM进行提取与度量,将输出的检索结果标记为一个图像集。随后,提取查询图像与图像集中每个目标的ART和EH特征;利用Euclidean距离分别度量查询图像与图像集中目标的ART与EH的相似性;最后,对ART与EH的加权组合,输出相似性最高的检索图像。结果 实验表明,与当前常见的检索算法比较,文中算法具有更高的检索精度,表现出更优异的Precision-Recall曲线。结论 所提算法具有良好的检索准确度,在信息处理、包装商标等领域具有一定的参考价值。