基于查询扩展的人名消歧

针对现有很多基于特征的人名消歧方法不适用于文档本身特征稀疏的问题,提出一种借助丰富的互联网资源,使用搜索引擎查询并扩展出更多与文档相关特征的方法。首先根据搜索引擎的特性构建了四类查询规则,然后通过这些查询规则进行搜索并返回前k个文档,最后对这些文档使用文档频率(DF)方法进行特征选择,并将选择的特征加入到原文档中。实验证明,该方法能显著提高人名消歧系统的性能,平均F值由76%增加到81%。     

基于语义的概念查询扩展

针对当前信息检索系统中所存在查准率低和查全率低的情况,分析了当前检索系统中常用的方法后,提出了一种基于语义的概念查询扩展方法.该方法结合概念语义空间来实现用户检索的概念查询扩展,以达到提高查准率和查全率的目的.实验结果表明,该方法相对于传统方法可以大幅提高用户检索的查准率和查全率.     

Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.     

基于文档平滑和查询扩展的文档敏感信息检测方法

由于办公终端可能出现敏感信息泄露的风险,对终端上的文档进行敏感信息检测就显得十分重要,但现有敏感信息检测方法中存在上下文信息无关的索引导致文档建模不准确、查询语义扩展不充分的问题。为此,首先提出基于上下文的文档索引平滑算法,构建尽可能保留文档信息的索引;然后改进查询语义扩展算法,结合领域本体中概念敏感度适当扩大敏感信息检测范围;最后将文档平滑和查询扩展融合于语言模型,在其基础上提出了文档敏感信息检测方法。将采用不同索引机制、查询关键字扩展算法及检测模型的四种方法进行比较,所提出的算法在文档敏感信息检测中的查全率、准确率和F值分别为0.798,0.786和0.792,各项性能指标均明显优于对比算法。结果表明该算法是一种能更有效检测敏感信息的方法。     

基于主题的个性化查询扩展模型

针对多义词和词典问题,结合文本分析和用户行为分析,提出了一种基于主题的个性化查询扩展模型.分析文本时,结合关联规则和图排序算法构建TextRank模型,脱离了对人工词典的依赖,并用此模型提取多文本主题;在用户行为分析上,使用移动时间窗口法建立用户模型,有效地捕获了当前的查询主题.查询扩展时,匹配用户主题与文本主题,选择相应的关联规则进行扩展.对结合关联规则与图排序的主题提取进行了实验,并将基于主题的查询扩展模型与其它查询扩展模型进行了比较.     

.基于用户查询扩展的自动摘要技术*

提出了一种新的文档自动摘要方法,利用非负矩阵分解算法将原始文档表示为若干语义特征向量的线性组合,通过相似性计算来确定与用户查询高度相关的语义特征向量,抽取在该向量上具有较大投影系数的句子作为摘要,在此过程中,多次采用相关反馈技术对用户查询进行扩展优化。实验表明,该方法所得摘要在突出文档主题的同时,体现了用户的需求和兴趣,有效改善了信息检索的效率。     

Information assimilation framework for event detection in multimedia surveillance systems

Most multimedia surveillance and monitoring systems nowadays utilize multiple types of sensors to detect events of interest as and when they occur in the environment. However, due to the asynchrony among and diversity of sensors, information assimilation – how to combine the information obtained from asynchronous and multifarious sources is an important and challenging research problem. In this paper, we propose a framework for information assimilation that addresses the issues – “when”, “what” and “how” to assimilate the information obtained from different media sources in order to detect events in multimedia surveillance systems. The proposed framework adopts a hierarchical probabilistic assimilation approach to detect atomic and compound events. To detect an event, our framework uses not only the media streams available at the current instant but it also utilizes their two important properties – first, accumulated past history of whether they have been providing concurring or contradictory evidences, and – second, the system designer’s confidence in them. The experimental results show the utility of the proposed framework.     

Evaluating multimedia features and fusion for example-based event detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported.     

Pseudo-relevance feedback based query expansion using boosting algorithm

Artificial Intelligence Review - Retrieving relevant documents from a large set using the original query is a formidable challenge. A generic approach to improve the retrieval process is realized...     

基于词向量的专利自动扩展查询研究

专利检索与普通的文本检索有着极大的不同,专利文本包括权利声明、摘要、全文等不同部分,自然不能简单地将普通文本的检索方法应用到专利检索当中来。专利检索通常面临着召回率低下的问题,首先,由于专利文本具有极强的专业性,有着复杂的术语表达方式,用户输入的关键词通常无法明确捕捉到检索意图,导致检索结果不理想。其次,专利撰写时有意识地制造与众不同的词汇,导致相关专利无法被检索到。目前有很多的研究方法都旨在提高专利检索的召回率,但是仍然有许多问题有待解决,检索效果有待改善。提出了一个基于词向量的专利自动扩展查询方法,在词向量的基础上,构建一个关键词查询网络,通过稠密子图发现算法来寻找扩展词集合,提高扩展词的有效性。在CLEF-IP 2012数据集的基础上进行了充分的实验,实验结果表明,本文提出的算法能够保证扩展词集获取的灵活性和有效性,同时能进一步提高专利检索的召回率。     

Ontology based user query interpretation for semantic multimedia contents retrieval

Users who are familiar with the existing keyword-based search have problems of not being able to configure the formal query because they don’t have generic knowledge on knowledge base when using the semantic-based retrieval system. User wants the search results which are more accurate and match the user’s search intents with the existing keyword-based search and the same search keyword without the need to recognize what technology the currently used retrieval system is based on to provide the search results. In order to do the semantic analysis of the ambiguous search keyword entered by users who are familiar with the existing keyword-based search, ontological knowledge base constructed based on refined meta-data is necessary, and the keyword semantic analysis technique which reflects user’s search intents from the well-established knowledge base and can generate accurate search results is necessary. In this paper, therefore, by limiting the knowledge base construction to multimedia contents meta-data, the applicable prototype has been implemented and its performance in the same environment as Smart TV has been evaluated. Semantic analysis of user’s search keyword is done, evaluated and recommended through the proposed ontological knowledge base framework so that accurate search results that match user’s search intents can be provided.     

E-LAMP: integration of innovative ideas for multimedia event detection

Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED’11 and MED’12 demonstrate the excellent performance of the integration of these ideas.     

基于领域本体知识库的语义查询扩展

为了在检索过程中全面表达用户查询意图,提出了基于领域本体知识库的语义查询扩展方法。该方法借助领域本体推理出的知识,使检索系统从语义层面理解用户查询语句,并通过语义相似度来控制扩展词的规模,避免了查询过度扩展,使得新构造的查询能更准确地描述用户的检索需求,提高了检索的有效性。原型系统的实验结果表明,该方法较传统的关键字匹配法和LAC方法有明显的优势,在保障查全率的基础上,可极大地提高检索准确率。     

An evolutionary game theory based approach for query expansion

Multimedia Tools and Applications - In Information Retrieval (IR) Systems, an essential technique employed to improve accuracy and efficiency is Query Expansion (QE). QE is the technique that...     

基于互信息的问句语义扩展研究

用户习惯用很少的关键字来检索所需的信息,这必然会导致出现用户所检索的信息与得到的信息有所偏差.针对这一现象,提出了基于互信息的问句语义扩展模型(QSE_BMI).它的好处在于可以根据用户自己制定的兴趣模型和输入的查询问句,检索出与用户兴趣相匹配的并且符合用户需要的相关信息.     

基于有色网的RFID复杂事件检测模型的研究与分析*

摘 要: 复杂事件处理技术是射频识别数据管理的有效技术之一,越来越受到业界的广泛关注。针对RFID数据的特性以及目前几种复杂事件检测模型的不足,在原有基于Petri网模型的基础上,通过扩展和改进提出了一种基于有色网的RFID事件检测网EDN,对事件操作符进行了形式化和图形化的描述,建立了复杂事件表达式的层次结构模型,并分析了复杂事件表达式中的冲突算法,给出了RFID事件的资源回收机制,提高了RFID事件的处理能力。通过实验证明了该模型的正确性和有效性。     

一种个性化查询扩展方法

针对当前主流web搜索引擎存在信息检索个性化效果差和信息检索的精确率低等缺点, 通过对已有方法的技术改进, 介绍了一种基于用户历史兴趣网页和历史查询词相结合的个性化查询扩展方法。当用户在搜索引擎上输入查询词时,能根据学习到的当前用户兴趣模型动态判定用户潜在兴趣和计算词间相关度,并将恰当的扩展查询词组提交给搜索引擎,从而实现不同用户输入同一查询词能返回不同检索结果的目的。实验验证了算法的有效性,检索精确率也比原方法有明显提高。     

基于DNS查询行为的Bot检测

提出一种基于DNS查询行为的检测方法。根据Bot的自动运行特性,从DNS查询的角度对主机中的进程进行初步过滤,缩小检测范围;分析Bot与其他进程的DNS反应行为模式的异同,构建Bot-DNS检测模型,在此基础上判断可疑进程是否为Bot。实验结果表明,该方法能够检测出处于生命周期早期阶段的Bot,且检测过程与Bot采用的协议结构无关,具有较好的检测效果。     

一种基于哈希函数及能量均衡的事件查询算法

在无线传感器网络中对于无固定位置的事件及查询是个重要的研究课题。结合高效及最大化网络生命周期,提出了一种基于哈希函数及能量均衡的事件查询算法。在该算法中,一个传感器节点只需要关心自己通信范围内的邻居节点,不需要知道整个网络的状况,算法具有冗余数据少、查询能耗小、网络生命周期长、实现简单等特点。借助OMNET++网络模拟器进行仿真实验,与经典路由算法比较,结果表明本算法能快速高效地进行事件查询,同时最小化及均衡能量消耗,延长了网络生命周期。