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21.
This paper describes a database model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough relational database model also incorporates indiscernibility in the representation and in all the operators of the rough relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain. There are several obvious applications for which the rough relational database model can more accurately model an enterprise than does the standard relational model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough relational database. In addition, applications inherently “rough” could be more easily implemented and maintained in the rough relational database. 相似文献
22.
随着企业内部网络应用的深入,Intranet内部的信息资源越来越庞大,怎样为这些数据信息构建索引是我们面If缶的主要任务,而全文检索的产生解决了这一问题。目前越来越多的中小企业采用Linux系统作为Web平台,并且利用检索系统来管理内部繁多的文本和HTML文件。文章对www搜索引擎的全文检索及其相关技术进行了分析和讨论,实现了一个基于Linux环境的Intranet搜索引擎LISE(Linux Intranet Search Engine)。LISE利用了基于词表的索引方法,为用户提供了更加准确的信息,能满足多种中小企业用户的需求。 相似文献
23.
提出一种根据用户指定的人体运动和观察视角生成真实感视频的方法.首先采集演员进行少数基本运动时的多视角视频数据库,并使用无标记运动捕捉的方法获得任意时刻人体对应的骨骼和3D模型.其次,用户对人体骨架指定运动并设定视角,以此定义目标视频.实验结果验证了文中方法能够利用有限的数据库合成演员在用户指定运动和视角下的真实感视频. 相似文献
24.
针对原有强度传输方程法所恢复的相位精度不够精确的缺点,提出强度传输方程和神经网络融合的三维重构算法. 利用强度传输方程求解出物体不同角度的初始相位,利用神经网络算法进行优化,根据不同角度的最终恢复相位结合乘法技术重构出三维体信息. 该算法具有精度高的特点,可以为三维成像技术的应用提供参考. 对于实验中的示例图像,该算法将强度传输方程得到的相位误差从21.40%降低为5.26%,重构三维物体与模拟真实物体的相关程度为显著相关. 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
28.
Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate “multi-instance bags (MI-bags)”, which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art. 相似文献
29.
《Computer Speech and Language》2014,28(6):1287-1297
This paper proposes an efficient speech data selection technique that can identify those data that will be well recognized. Conventional confidence measure techniques can also identify well-recognized speech data. However, those techniques require a lot of computation time for speech recognition processing to estimate confidence scores. Speech data with low confidence should not go through the time-consuming recognition process since they will yield erroneous spoken documents that will eventually be rejected. The proposed technique can select the speech data that will be acceptable for speech recognition applications. It rapidly selects speech data with high prior confidence based on acoustic likelihood values and using only speech and monophone models. Experiments show that the proposed confidence estimation technique is over 50 times faster than the conventional posterior confidence measure while providing equivalent data selection performance for speech recognition and spoken document retrieval. 相似文献
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