共查询到20条相似文献,搜索用时 15 毫秒
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Ronaldo A Ferreira Mehmet Koyutürk Suresh Jagannathan Ananth Grama 《Journal of Parallel and Distributed Computing》2008
The past few years have seen tremendous advances in distributed storage infrastructure. Unstructured and structured overlay networks have been successfully used in a variety of applications, ranging from file-sharing to scientific data repositories. While unstructured networks benefit from low maintenance overhead, the associated search costs are high. On the other hand, structured networks have higher maintenance overheads, but facilitate bounded time search of installed keywords. When dealing with typical data sets, though, it is infeasible to install every possible search term as a keyword into the structured overlay. 相似文献
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Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, in order to deal with the data of different modalities in terms of their characteristics, we present a semantic annotation model which employs continuous PLSA and standard PLSA to model visual features and textual words respectively. The model learns the correlation between these two modalities by an asymmetric learning approach and then it can predict semantic annotation precisely for unseen images. Finally, we compare our approach with several state-of-the-art approaches on the Corel5k and Corel30k datasets. The experiment results show that our approach performs more effectively and accurately. 相似文献
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Jürgen Assfalg Marco Bertini Carlo Colombo Alberto Del Bimbo Walter Nunziati 《Computer Vision and Image Understanding》2003,92(2-3):285
Automatic semantic annotation of video streams allows both to extract significant clips for production logging and to index video streams for posterity logging. Automatic annotation for production logging is particularly demanding, as it is applied to non-edited video streams and must rely only on visual information. Moreover, annotation must be computed in quasi real-time. In this paper, we present a system that performs automatic annotation of the principal highlights in soccer video, suited for both production and posterity logging. The knowledge of the soccer domain is encoded into a set of finite state machines, each of which models a specific highlight. Highlight detection exploits visual cues that are estimated from the video stream, and particularly, ball motion, the currently framed playfield zone, players’ positions and colors of players’ uniforms. The highlight models are checked against the current observations, using a model checking algorithm. The system has been developed within the EU ASSAVID project. 相似文献
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Semantic annotation for knowledge management: Requirements and a survey of the state of the art 总被引:1,自引:0,他引:1
While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress. 相似文献
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基于图像分割的语义标注方法 总被引:1,自引:0,他引:1
为有效解决图像检索中存在的“语义鸿沟”问题,提出了一种新的语义标注方法。该方法以图像分割为基础,在训练阶段构建图像字典,通过对图像单元颜色、纹理、小波轮廓的分析和描述形成一种结合小波轮廓比对和概率统计的二阶段标注模型,模型针对不同类别的图像分阶段采用相应的标注方法。经实验,应用该模型进行图像检索查全率和查准率都有明显提高,其中查准率最高可提升23.6%,证明该方法更接近人对图像内容的理解,具有良好的标注效果和检索性能。 相似文献
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基于个性化本体的图像语义标注和检索 总被引:1,自引:0,他引:1
针对目前图像检索系统较难实现语义检索的问题,提出了一种新的以本体为核心的图像语义标注和检索模型。构建个性化本体描述图像语义,继而提取基于概念集的图像语义特征并利用本体中“Is-A”关系设计相似性度量方法最终实现语义扩展检索。其难点在于顶级本体向个性化本体进化,以及基于概念集和“Is-A”关系实现语义相似度量的方法。通过系统的初步实现与相关实验的验证,该模型的检索准确度可达88.6%,明显高于传统的基于关键字和基于通用本体的图像检索,实现了图像智能检索功能。 相似文献
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Personalized retrieval of sports video based on multi-modal analysis and user preference acquisition
In this paper, we present a novel framework on personalized retrieval of sports video, which includes two research tasks:
semantic annotation and user preference acquisition. For semantic annotation, web-casting texts which are corresponding to
sports videos are firstly captured from the webpages using data region segmentation and labeling. Incorporating the text,
we detect events in the sports video and generate video event clips. These video clips are annotated by the semantics extracted
from web-casting texts and indexed in a sports video database. Based on the annotation, these video clips can be retrieved
from different semantic attributes according to the user preference. For user preference acquisition, we utilize click-through
data as a feedback from the user. Relevance feedback is applied on text annotation and visual features to infer the intention
and interested points of the user. A user preference model is learned to re-rank the initial results. Experiments are conducted
on broadcast soccer and basketball videos and show an encouraging performance of the proposed method.
Yi-Fan Zhang received the B.E. degree from Southeast University, Nanjing, China, in 2004. He is currently pursuing the Ph.D. degree at National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. In 2007, he was an intern student in Institute for Infocomm Research, Singapore. Currently he is an intern student in China-Singapore Institute of Digital Media. His research interests include multimedia, video analysis and pattern recognition. Changsheng Xu (M’97–SM’99) received the Ph.D. degree from Tsinghua University, Beijing, China in 1996. Currently he is Professor of Institute of Automation, Chinese Academy of Sciences and Executive Director of China-Singapore Institute of Digital Media. He was with Institute for Infocomm Research, Singapore from 1998 to 2008. He was with the National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences from 1996 to 1998. His research interests include multimedia content analysis, indexing and retrieval, digital watermarking, computer vision and pattern recognition. He published over 150 papers in those areas. Dr. Xu is an Associate Editor of ACM/Springer Multimedia Systems Journal. He served as Short Paper Co-Chair of ACM Multimedia 2008, General Co-Chair of 2008 Pacific-Rim Conference on Multimedia (PCM2008) and 2007 Asia-Pacific Workshop on Visual Information Processing (VIP2007), Program Co-Chair of VIP2006, Industry Track Chair and Area Chair of 2007 International Conference on Multimedia Modeling (MMM2007). He also served as Technical Program Committee Member of major international multimedia conferences, including ACM Multimedia Conference, International Conference on Multimedia & Expo, Pacific-Rim Conference on Multimedia, and International Conference on Multimedia Modeling. Xiaoyu Zhang received the B.S. degree in computer science from Nanjing University of Science and Technology in 2005. He is a Ph.D. candidate of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. He is currently a student in China-Singapore Institute of Digital Media. His research interests include image retrieval, video analysis, and machine learning. Hanqing Lu (M’05–SM’06) received the Ph.D. degree in Huazhong University of Sciences and Technology, Wuhan, China in 1992. Currently he is Professor of Institute of Automation, Chinese Academy of Sciences. His research interests include image similarity measure, video analysis, object recognition and tracking. He published more than 100 papers in those areas. 相似文献
Hanqing LuEmail: |
Yi-Fan Zhang received the B.E. degree from Southeast University, Nanjing, China, in 2004. He is currently pursuing the Ph.D. degree at National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. In 2007, he was an intern student in Institute for Infocomm Research, Singapore. Currently he is an intern student in China-Singapore Institute of Digital Media. His research interests include multimedia, video analysis and pattern recognition. Changsheng Xu (M’97–SM’99) received the Ph.D. degree from Tsinghua University, Beijing, China in 1996. Currently he is Professor of Institute of Automation, Chinese Academy of Sciences and Executive Director of China-Singapore Institute of Digital Media. He was with Institute for Infocomm Research, Singapore from 1998 to 2008. He was with the National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences from 1996 to 1998. His research interests include multimedia content analysis, indexing and retrieval, digital watermarking, computer vision and pattern recognition. He published over 150 papers in those areas. Dr. Xu is an Associate Editor of ACM/Springer Multimedia Systems Journal. He served as Short Paper Co-Chair of ACM Multimedia 2008, General Co-Chair of 2008 Pacific-Rim Conference on Multimedia (PCM2008) and 2007 Asia-Pacific Workshop on Visual Information Processing (VIP2007), Program Co-Chair of VIP2006, Industry Track Chair and Area Chair of 2007 International Conference on Multimedia Modeling (MMM2007). He also served as Technical Program Committee Member of major international multimedia conferences, including ACM Multimedia Conference, International Conference on Multimedia & Expo, Pacific-Rim Conference on Multimedia, and International Conference on Multimedia Modeling. Xiaoyu Zhang received the B.S. degree in computer science from Nanjing University of Science and Technology in 2005. He is a Ph.D. candidate of National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. He is currently a student in China-Singapore Institute of Digital Media. His research interests include image retrieval, video analysis, and machine learning. Hanqing Lu (M’05–SM’06) received the Ph.D. degree in Huazhong University of Sciences and Technology, Wuhan, China in 1992. Currently he is Professor of Institute of Automation, Chinese Academy of Sciences. His research interests include image similarity measure, video analysis, object recognition and tracking. He published more than 100 papers in those areas. 相似文献
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Ruofei Zhang Zhongfei Zhang Mingjing Li Wei-Ying Ma Hong-Jiang Zhang 《Multimedia Systems》2006,12(1):27-33
This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature. 相似文献
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提出一种基于改进的k-means算法的图像标注和检索方法。首先对训练图像进行分割,采用改进的k-means算法对分割后的区域进行聚类。改进的k-means算法首先采用遗传聚类算法确定聚类数k,然后对聚类中心进行选择。在图像标注时,首先通过已标注的图像求出语义概念和聚类区域的关联度,用它作为待标注图像的先验知识,然后结合区域的低层特征,对未标注的图像进行标注。在一个包含1 000幅图像的图像库进行实验,采用标注的语义关键字进行检索,结果表明,提出的方法是有效的。 相似文献
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This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus documents are homogeneously represented using statistical language models. Under this framework, we can perform all the necessary operations for an efficient and effective semantic annotation of the corpus. Firstly, we propose a coarse tailoring of the KRs w.r.t the target corpus with the main goal of reducing the ambiguity of the annotations and their computational overhead. Then, we propose the generation of concept profiles, which allow measuring the semantic overlap of the KRs as well as performing a finer tailoring of them. Finally, we propose how to semantically represent documents and queries in terms of the KRs concepts and the statistical framework to perform semantic search. Experiments have been carried out with a corpus about web resources which includes several Life Sciences catalogs and Wikipedia pages related to web resources in general (e.g., databases, tools, services, etc.). Results demonstrate that the proposed method is more effective and efficient than state-of-the-art methods relying on either context-free annotation or keyword-based search. 相似文献
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《Expert systems with applications》2014,41(18):8225-8233
The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems. 相似文献
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James Z. Wang Kurt Grieb Ya Zhang Ching-chih Chen Yixin Chen Jia Li 《International Journal on Digital Libraries》2006,6(1):18-29
Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for
many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval
of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic
Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images
of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated
representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models
in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood
of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental
results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection.
Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution
copyright-protected images.
A preliminary version of this work has been presented at the DELOS-NSF Workshop on Multimedia in Digital Libraries, Crete, Greece, June 2003. The work was completed when Kurt Grieb and Ya Zhang were students of The Pennsylvania State University.
James Z. Wang and Jia Li are also affiliated with Department of Computer Science and Engineering, The Pennsylvania State University.
Yixin Chen is also with the Research Institute for Children, Children's Hospital, New Orleans. 相似文献
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In this paper, we describe the first hybrid Semantic Web service matchmaker for OWL-S services, called OWLS-MX. It complements crisp logic-based semantic matching of OWL-S services with token-based syntactic similarity measurements in case the former fails. The results of the experimental evaluation of OWLS-MX provide strong evidence for the claim that logic-based semantic matching of OWL-S services can be significantly improved by incorporating non-logic-based information retrieval techniques. An additional analysis of false positives and false negatives of the hybrid matching filters of OWLS-MX led to an even further improved matchmaker version called OWLS-MX2. 相似文献
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针对目前矿山领域异构数据融合时先验知识获取困难、物联网本体库实时性差、实例对象数据手动标注方式效率较低等问题,提出了一种矿山语义物联网自动语义标注方法。给出了传感数据语义化处理框架:一方面,确定本体的专业领域和范畴,通过重用流注释本体(SAO)构建领域本体,作为驱动语义标注的基础;另一方面,使用机器学习方法对感知数据流进行特征提取与数据分析,从海量数据中挖掘出概念间的关系;通过数据挖掘知识来驱动本体的更新与完善,实现本体的动态更新、拓展与更精确的语义标注,增强机器的理解力。以矿井提升系统主轴故障为例阐述从本体到实例化的语义标注过程:结合领域专家知识及本体重用,采用"七步法"建立矿井提升系统主传动故障本体;为了加强实例数据属性描述的准确性,使用主成分分析法(PCA)与K-means聚类方法对数据集进行降维和分组,提取出数据属性与概念的关系;通过基于语义Web的规则语言(SWRL)标注具体先行条件与后续概念的关系,优化领域本体。实验结果表明:在本体实例化过程中,可利用机器学习技术从传感数据中自动提取概念,实现传感数据的自动语义标注。 相似文献