Isolation,characterization, and genomic analysis of the novel T4-like bacteriophage ΦCJ20

Food Science and Biotechnology - Pathogenic Escherichia coli infections have been consistently reported annually. The basic characteristics and genome of the newly isolated ΦCJ20 from swine...     

Web资源的多粒度语义标注及其应用技术研究

当前的Web搜索引擎获得的搜索结果都是基于关键字标注的Web文档、页面或链接,不支持对文档内部信息的检索。为支持Wcb资源内部信息的检索,研究多粒度语义标注,即按树根结点、分支结点、叶子结点及资源信息元为粒度单位对Web资源进行组织管理,并在此基础上探讨基于本体的搜索技术。初步的分析和实验表明,这样可以提高从形式多样的海量Web资源中获取所需信息的效率。     

Structural image retrieval using automatic image annotation and region based inverted file

Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.     

Image annotation using high order statistics in non-Euclidean spaces

Automatic image annotation is a promising way to achieve more effective image retrieval and image analysis by using keywords associated to the image content. Due to the semantic gap between low-level visual features and high-level semantic concepts of an image, however, the performances of many existing algorithms are not so satisfactory. In this paper, a novel image classification scheme, named high order statistics based maximum a posterior (HOS-MAP), is proposed to deal with the issue of image annotation. To bridge the gap between human judgment and machine intelligence, the proposed scheme first constructs a dissimilarity representation for each image in a non-Euclidean space; then, the information of dissimilarity diffusion distribution for each image is achieved with respect to the high-order statistics of a triplet of nearest neighbor images; finally, a maximum a posteriori algorithm with the information of Gaussian Mixture Model and dissimilarity diffusion distribution is adopted to estimate the relevance between each annotation and an input un-annotated image. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed automatic image annotation scheme.     

支持背景知识的多维端到端短语识别算法研究

目前,实体识别与依存关系分析,采用的主要是基于监督学习的深度端到端方法.这种方法存在两个问题:不能引入背景知识;不能识别出自然语言的多粒度、嵌套特征.为了解决以上问题,提出了基于短语窗口的依存句法标注规则,并标注了中文短语窗口数据集(CPWD),同时设计了配套的多维端到端短语识别模型(MDM模型).该标注规则以短语为最...     

试论翻译中的文化补偿

本文论述了文化补偿的四种方法.由于原语和目的语文化之间存在的差异性和不对应性,译者应在忠实于原文的基础上,采用必要的补偿手段,以充分传达原文信息,在原语和译语之间取得最大限度的等值.     

基于贝叶斯通用背景模型的图像标注

在高斯图特征提取过程中,通用背景模型(Universal background model, UBM) 方法常用于根据总体分布估计每一幅图像中特征点分布的高斯混合模型(Gaussian mixture model, GMM)参数. 然而UBM估计的GMM权重参数中有很多接近零的数值,它们所对应的高斯分量对分布估计贡献小却又都参与了计算, 因此UBM的时间复杂度较高. 为解决这个问题,本文提出Bayes UBM方法. 通过引入受限的对称Dirichlet分布来描述GMM权重参数的先验分布,利用Bayes最大后验概率对GMM参数集进行估计. 实验表明Bayes UBM方法不仅有效地降低了时间复杂度,而且提高了Corel数据集上的图像标注精度.     

基于中文电子病历的心血管疾病风险因素标注体系及语料库构建

本文讨论了从中文电子病历中标注心血管疾病风险因素及其相关信息的问题,提出了适应中文电子病历内容特点的心血管疾病风险因素标注体系,构建了中文健康信息处理领域首份关于心血管疾病风险因素的标注语料库.     

通过语义控制改进运动合成

运动相似性度量是基于运动图的人体运动合成技术的关键.现有方法主要使用运动捕捉数据姿态数值特征进行相似性度量与合成控制,很难处理语义描述上同类型运动数据集中的不同运动实现版本间的时间与空间特征分布变化的问题.提出了引入用户语义控制来改进基于运动图的人体运动合成方法.使用关系特征作为高层语义描述,刻画同类型运动的空间变化特征;通过自学习过程,获得运动类模板,作为同类型运动的空-时特征语义描述;通过将运动类模板数据与人体运动序列文件进行匹配,实现运动类型识别和自动语义信息标注;借助关系特征语义描述及运动序列文件的语义标注信息,实现在基于运动图的运动合成中引入用户直观的高层语义控制.运动合成实验结果显示了该方法的有效性,为获得高质量人体运动合成数据提供途径.     

基于情感感知的面料图像数据库系统的设计与实现

在面料图像数据库系统中,为了让计算机对面料图像的情感语义进行自动感知,帮助用户用情感语义浏览或挑选所需的面料,并对其描述进行数值标注,文章针对面料图像本身表述的情感语义进行研究,进而实现基于情感语义的图像检索。在利用支持向量机等技术通过VC++编程实现面料图像情感语义识别的基础上,通过动态链接库技术将情感识别功能应用到采用NET编程开发的面料图像数据库系统中,在面料数据库系统中实现了计算机对面料图像情感语义的自动感知和识别,并进行情感语义的标注,从而实现了基于情感语义的面料图像检索。经过测试,该系统达到了较好的识别和分类效果。