动物标本数字博物馆的设计与实现

本文给出了一个面向用户的动物标本综合检索系统的设计与实现,重点实现动物标本相关信息的智能检索。用户可按科、属、种、和产地等文字信息方便地进行检索动物标本相关信息;并可根据用户提交的查询示例图像,利用图像的颜色、纹理等视觉特征实现基于内容的动物标本图像检索功能;通过利用三维扫描仪获得动物标本的3D模型并结合使用中视典的VRP软件实现在web上发布,用户可以通过IE浏览器访问和控制3D模型,解决了动物标本的3D建模技术。     

基于新疆地方性高发病肝包虫病CT图像灰度直方图特征检索方法的研究

新疆地方性肝包虫病是新疆牧区发病率较高的传染性寄生虫病,严重影响牧区各族人民生活质量的提高.根据图像特征,选择合适距离算法,实现快速准确地图像检索,对辅助医生早期发现、诊断和治疗肝包虫病有重大意义.本文研究比较了使用肝包虫病CT医学图像灰度直方图特征进行图像检索时,不同距离算法的有效性.实验结果表明:对于肝包虫病医学图像的基于灰度直方图特征的图像检索,马氏距离算法与标准欧氏距离算法比其他距离算法较优.     

一种基于松弛特征关联的3D人脸多特征匹配算法

针对人脸特征表情变化极为复杂,3D模型形变中的程度较为细微.一些特征点的位置会出现一定程度的飘逸,给准确的三维特征匹配带来了难度.提出基于松弛特征关联的3D人脸多特征匹配算法.通过提取特征点,计算三维特征的松弛特征对应程度,最大程度的完成在误差较大程度下的三维特征匹配.避免传统算法中用直线距离特征匹配带来的弊端.实验仿真程序表明:该方法在误差较大的匹配环境下,优化效果较为明显.     

基于颜色和纹理特征的彩色图像检索方法

由于单一特征不足以准确地描述图像的特征,文章提出了一种新的彩色图像检索方法,该方法在对图像进行分块的基础上,分别计算图像R、G、B三通道的均值和方差,获取了图像的颜色统计特征;然后通过小波包变换提取纹理特征。在进行图像间的相似性测量时,为了结合不同的特征进行全局的相似性检索,还采用Guassian模型对不同特征间的距离进行了归一化处理。仿真结果表明,提出的方法具有良好的检索效果。     

三维模型检索算法综述

三维模型检索算法种类繁多,基于内容的三维模型检索算法是目前研究的主要领域。基于内容的检索方法首先计算三维模型数据库中的所有模型和待检索模型的特征描述符,然后通过比较待检索模型与数据库中所有模型的特征描述符的相似度,获取与待检索模型最相似的模型,实现对三维模型的检索：     

内容图像检索IALA算法的设计与实现

基于图像检索的距离度量算法主要有两类:一种是最优化方法,其代表算法是旅行商距离(EMD);另一种是统计方法,其代表算法是渐进似然估计(ALA)距离.IALA图像检索算法利用分层的方法来提高检索精度,既克服了最优化方法EMD算法对混合成分发散的源图像的检索效果不佳的缺点,又对传统ALA算法对高斯混合模型方差较大的数据库图像产生误判的不足之处进行了改进.实验证明IALA图像检索算法可以大大提高检索的效率和精度.     

模糊c均值聚类在wav格式音频检索中的研究

应用模糊c均值聚类方法对wav格式的音频检索。不同于以前为音频建模和分类的监督机制,而是通过对音频数据的特征进行模糊聚类形成聚类质心,根据聚类质心的相似度匹配来实现非监督机制的音频检索,并优化参数以提高检索准确度。     

新型信息检索模型发展研究

介绍了3个新型信息检索模型——信念网络模型、粗糙集理论检索模型和遗传算法检索模型。认为信念网络模型以概率推理为基础,推理结果说服力强,并采用图形化网络结构直观地表达变量的联合概率分布及其条件独立性,能大量节约概率推理计算;粗糙集理论检索模型通过不可分辨关系确定问题的近似域,对问题不确定性的描述和处理具有客观性;遗传算法检索模型模仿自然界中生物的遗传和进化机理,可以方便地应用遗传操作算子,具有独特的优越性。这3种检索模型由于理论独特、方法新颖和效果良好,已经得到了一定的应用,逐渐成为研究热点。     

基于空间聚类的图像检索方法

信息检索在信息飞速增长的今天具有十分重大的意义。作为信息检索的一个重要的部分,图像检索得到了广泛的研究。由于基于文本的图像检索的种种不利因素,基于内容的图像检索成为目前的主流方向,本文提出一种基于空间聚类的方法,用图像的颜色特征来计算图像之间距离,采用DBSCAN算法对图像聚类,得到检索结果。     

科技查新预检索及医学实例分析

科技查新是我国科技创新服务工作的一个重要环节,科技查新预检索对作好科技查新工作至关重要。笔者探讨了科技查新预检索数据库选择、预检索方法、预检索结果处理等,并就两种主题概念检索方法(主题词检索和关键词检索)举例,进行比较分析。     

基于用户体验的可视化信息检索模型及界面研究

人类进入信息时代尤其是进入大数据时代后,信息数量急剧增长,传统信息检索方式由于其自身不足已经满足不了人们的要求。在用户体验为中心的时代,本文通过对比传统信息检索和可视化信息检索的概念、模型,提出了基于用户体验的可视化信息检索的概念、模型和检索界面,对于提高检索效率和准确性有一定的帮助。     

网络环境下的信息检索与数据挖掘技术

首先对网络环境下信息检索的现状进行分析。主要介绍网络信息检索的代表工具一搜索引擎的工作原理、缺陷夏发展方向,引出数据挖掘技术。并进一步对WEB数据挖掘技术作了概要的介绍,阐明WEB数据挖掘技术是网络信息检索智能化的重要发展方向之一。最后,提出一个结合数据挖掘技术的新的搜索引擎结构模型。     

A study of awareness in multimedia search

Awareness of another’s activity is an important aspect of facilitating collaboration between users, enabling an “understanding of the activities of others” (Dourish & Bellotti, 1992). In this paper we investigate the role of awareness and its effect on search performance and behaviour in collaborative multimedia retrieval. We focus on the scenario where two users are searching at the same time on the same task, and via an interface, can see the activity of the other user. The main research question asks: does awareness of another searcher aid a user when carrying out a multimedia search session?To encourage awareness, an experimental study was designed where two users were asked to compete to find as many relevant video shots as possible under different awareness conditions. These were individual search (no awareness), Mutual awareness (where both users could see the other’s search screen), and unbalanced awareness (where one user is able to see the other’s screen, but not vice-versa). Twelve pairs of users were recruited, and the four worst performing TRECVID 2006 search topics were used as search tasks, under four different awareness conditions. We present the results of this study, followed by a discussion of the implications for multimedia information retrieval systems.     

基于医学知识图谱的慢性病在线医疗社区分面检索研究

[目的/意义]基于知识图谱与分面检索能够实现健康信息的有效组织,解决其多源异构、专业知识门槛高、语义歧义等方面的问题,从而帮助用户降低专业性医疗知识的使用门槛,引导用户更快获取资源。[方法/过程]将知识图谱与分面检索相结合,构建基于医学知识图谱的慢性病在线医疗社区分面检索模型,主要包括分面体系构建、分面与焦点排序以及分面展现控制3个步骤,并以百度贴吧自闭症吧为数据来源对分面检索原型予以实现。[结果/结论]所构建的自闭症分面检索原型系统应用效果较好,提高了用户检索的效率与质量。提出的分面检索模型对完善健康信息服务等相关理论和方法具有一定推动作用。     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.     

Mining and modeling linkage information from citation context for improving biomedical literature retrieval

Mining linkage information from the citation graph has been shown to be effective in identifying important literatures. However, the question of how to utilize linkage information from the citation graph to facilitate literature retrieval still remains largely unanswered. In this paper, given the context of biomedical literature retrieval, we first conduct a case study in order to find out whether applying PageRank and HITS algorithms directly to the citation graph is the best way of utilizing citation linkage information for improving biomedical literature retrieval. Second, we propose a probabilistic combination framework for integrating citation information into the content-based information retrieval weighting model. Based on the observations of the case study, we present two strategies for modeling the linkage information contained in the citation graph. The proposed framework provides a theoretical support for the combination of content and linkage information. Under this framework, exhaustive parameter tuning can be avoided. Extensive experiments on three TREC Genomics collections demonstrate the advantages and effectiveness of our proposed methods.     

一种基于主题和分众分类的信息检索优化方法

本文针对目前搜索引擎存在的检索结果缺乏组织导致检准率不高的问题,提出一种基于主题和分众分类的信息检索优化方法.首先对用户检索主题进行获取和表达,然后以社会标签为聚类项,采用向量空间模型实现基于分众分类的文档主题聚类,并将检索结果按相似度和标签"受欢迎度"复合排序,达到提高检索准确率和优化检索的效果.     

A reputation-based model for mobile agent migration for information search and retrieval

Mobile agent technology has been used in various applications including e-commerce, information processing, distributed network management, and database access. Information search and retrieval can be conducted by mobile agents in a decentralized system. As compared with the client/server model, the mobile agent approach has an advantage of saving network bandwidth and offering flexibility in information search and retrieval. In this paper, we present a model for mobile agents to select the most reputable information host to search and retrieve information. We use opinion-based belief structure to represent, aggregate and calculate the reputation of an information host. Since reputation is a multi-faced concept, our approach first allows the users to rank each information host's quality of service based on a set of evaluation categories. Then, a comprehensive, final reputation of the host is obtained by aggregating those specific category reputations. To recognize the subjective nature of a reputation, the transferable belief model is used to represent and rank the category reputation. Experiments are conducted using the Aglets technology to illustrate mobile agent migration.     

科技查新检索中的关键词选择

科技查新检索不同于一般文献检索,查新检索者要针对课题的查新点检索一系列的文献数据库,并对以此出具的查新报告的客观性、公正性承担法律责任。在浩如烟海的国内外文献中针对特定检索课题力求做到查准、查全并非易事,单就检索过程中的关键词选择就往往令人困惑!科技文献中普遍存在的一物多名（或同义词）是查新检索中的永恒难题。为了尽量减少漏检,查新者应对检索用关键词进行扩展,并以适当的检索策略覆盖关键词表达的盲区,必要时还应采用分类法检索作为主题法检索的补充,通过多种途径设法化解一物多名给科技文献资源共享带来的难题!科技查新本身是一种创造性的劳动。查新者应充满自信地敞开思路,用创造性的思维,深入自如地施展文献检索才艺。     

学科交叉与信息自动检索--科学发展呼唤新的信息自动检索工具

The intercrossing of disciphnes is an important impetus of scientific development. The existing automatic information retrieval systems are not propitious for the generation of new ideas and the interdisciplinary multiplication as they are developing towards more intelligent, knowledgeable and specialized ones. The scientific development calls for a new automatic information retrieval tool. The research will be a comprehensive subject involved with many disciplines and rields, and need to be conducted in order and step by step.