频繁模式集挖掘算法TFPDM的研究

受经典的Apriori算法思想和FP-Growth算法思想的启发,在结合两者优点的基础上提出了一种新的算法思想,它是对传统的FP-Growth算法的变形。该算法只需对数据库扫描一次,可以同时对全局和局部频繁模式集进行挖掘,减少了对发生增益数据库挖掘的费用。理论分析表明算法是有效的、可行的。     

An Integrated Framework for Semantic Annotation and Adaptation

Tools for the interpretation of significant events from video and video clip adaptation can effectively support automatic extraction and distribution of relevant content from video streams. In fact, adaptation can adjust meaningful content, previously detected and extracted, to the user/client capabilities and requirements. The integration of these two functions is increasingly important, due to the growing demand of multimedia data from remote clients with limited resources (PDAs, HCCs, Smart phones). In this paper we propose an unified framework for event-based and object-based semantic extraction from video and semantic on-line adaptation. Two cases of application, highlight detection and recognition from soccer videos and people behavior detection in domotic^* applications, are analyzed and discussed.Domotics is a neologism coming from the Latin word domus (home) and informatics.Marco Bertini has a research grant and carries out his research activity at the Department of Systems and Informatics at the University of Florence, Italy. He received a M.S. in electronic engineering from the University of Florence in 1999, and Ph.D. in 2004. His main research interest is content-based indexing and retrieval of videos. He is author of more than 25 papers in international conference proceedings and journals, and is a reviewer for international journals on multimedia and pattern recognition.Rita Cucchiara (Laurea Ingegneria Elettronica, 1989; Ph.D. in Computer Engineering, University of Bologna, Italy 1993). She is currently Full Professor in Computer Engineering at the University of Modena and Reggio Emilia (Italy). She was formerly Assistant Professor (‘93–‘98) at the University of Ferrara, Italy and Associate Professor (‘98–‘04) at the University of Modena and Reggio Emilia, Italy. She is currently in the Faculty staff of Computer Engenering where has in charges the courses of Computer Architectures and Computer Vision.Her current interests include pattern recognition, video analysis and computer vision for video surveillance, domotics, medical imaging, and computer architecture for managing image and multimedia data.Rita Cucchiara is author and co-author of more than 100 papers in international journals, and conference proceedings. She currently serves as reviewer for many international journals in computer vision and computer architecture (e.g. IEEE Trans. on PAMI, IEEE Trans. on Circuit and Systems, Trans. on SMC, Trans. on Vehicular Technology, Trans. on Medical Imaging, Image and Vision Computing, Journal of System architecture, IEEE Concurrency). She participated at scientific committees of the outstanding international conferences in computer vision and multimedia (CVPR, ICME, ICPR, ...) and symposia and organized special tracks in computer architecture for vision and image processing for traffic control. She is in the editorial board of Multimedia Tools and Applications journal. She is member of GIRPR (Italian chapter of Int. Assoc. of Pattern Recognition), AixIA (Ital. Assoc. Of Artificial Intelligence), ACM and IEEE Computer Society.Alberto Del Bimbo is Full Professor of Computer Engineering at the Università di Firenze, Italy. Since 1998 he is the Director of the Master in Multimedia of the Università di Firenze. At the present time, he is Deputy Rector of the Università di Firenze, in charge of Research and Innovation Transfer. His scientific interests are Pattern Recognition, Image Databases, Multimedia and Human Computer Interaction. Prof. Del Bimbo is the author of over 170 publications in the most distinguished international journals and conference proceedings. He is the author of the “Visual Information Retrieval” monography on content-based retrieval from image and video databases edited by Morgan Kaufman. He is Member of IEEE (Institute of Electrical and Electronic Engineers) and Fellow of IAPR (International Association for Pattern Recognition). He is presently Associate Editor of Pattern Recognition, Journal of Visual Languages and Computing, Multimedia Tools and Applications Journal, Pattern Analysis and Applications, IEEE Transactions on Multimedia, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He was the Guest Editor of several special issues on Image databases in highly respected journals.Andrea Prati (Laurea in Computer Engineering, 1998; PhD in Computer Engineering, University of Modena and Reggio Emilia, 2002). He is currently an assistant professor at the University of Modena and Reggio Emilia (Italy), Faculty of Engineering, Dipartimento di Scienze e Metodi dell’Ingegneria, Reggio Emilia. During last year of his PhD studies, he has spent six months as visiting scholar at the Computer Vision and Robotics Research (CVRR) lab at University of California, San Diego (UCSD), USA, working on a research project for traffic monitoring and management through computer vision. His research interests are mainly on motion detection and analysis, shadow removal techniques, video transcoding and analysis, computer architecture for multimedia and high performance video servers, video-surveillance and domotics. He is author of more than 60 papers in international and national conference proceedings and leading journals and he serves as reviewer for many international journals in computer vision and computer architecture. He is a member of IEEE, ACM and IAPR.     

Semantic Extraction and Semantics-Based Annotation and Retrieval for Video Databases

Digital video databases have become more pervasive and finding video clips quickly in large databases becomes a major challenge. Due to the nature of video, accessing contents of video is difficult and time-consuming. With content-based video systems today, there exists a significant gap between the user's information and what the system can deliver. Therefore, enabling intelligent means of interpretation on visual content, semantics annotation and retrieval are important topics of research. In this paper, we consider semantic interpretation of the contents as annotation tags for video clips, giving a retrieval-driven and application-oriented semantics extraction, annotation and retrieval model for video database management system. This system design employs an algorithm on objects' relation and it can reveal the semantics defined with fast real-time computation.     

基于约束的交互式频繁模式挖掘算法

频繁模式挖掘是数据挖掘领域中很重要的一部分.目前,出现了许多基于约束的频繁模式挖掘算法和交互式算法,但把两者结合起来的算法却很少.提出了一种基于约束的交互式频繁模式挖掘算法IMCFP(interactive mining of constraint-based frequent patterns).首先该算法按照约束的性质来建立频繁模式树,并且只需扫描一遍数据库;然后建立每个项的条件树,挖掘以该项为前缀的最大频繁模式,并用最大频繁模式树来存储;最后根据最大模式来找出所有的支持度明确的频繁模式.另外,该算法允许用户在挖掘过程中动态地改变约束.实验表明,该算法与iCFP算法相比是很有效的.     

A Novel Framework for Semantic Annotation and Personalized Retrieval of Sports Video

Sports video annotation is important for sports video semantic analysis such as event detection and personalization. In this paper, we propose a novel approach for sports video semantic annotation and personalized retrieval. Different from the state of the art sports video analysis methods which heavily rely on audio/visual features, the proposed approach incorporates web-casting text into sports video analysis. Compared with previous approaches, the contributions of our approach include the following. 1) The event detection accuracy is significantly improved due to the incorporation of web-casting text analysis. 2) The proposed approach is able to detect exact event boundary and extract event semantics that are very difficult or impossible to be handled by previous approaches. 3) The proposed method is able to create personalized summary from both general and specific point of view related to particular game, event, player or team according to user's preference. We present the framework of our approach and details of text analysis, video analysis, text/video alignment, and personalized retrieval. The experimental results on event boundary detection in sports video are encouraging and comparable to the manually selected events. The evaluation on personalized retrieval is effective in helping meet users' expectations.     

最大频繁模式的快速挖掘与更新算法

挖掘和更新最大频繁模式是多种数据挖掘应用中的关键问题。之前的许多研究都是采用Apriori类的候选生成-检验方法或基于FP-Tree的方法,而产生大量候选和动态创建大量FP-Tree的代价太高,特别是在支持度阈值较小或存在长模式时。因此,文章提出了一种最大频繁模式的快速挖掘算法DMFP及更新算法IUMFP。DMFP算法利用前缀树压缩存放数据,并通过调整前缀树中节点信息和节点链直接在前缀树上采用深度优先的策略进行挖掘,而不需要创建条件模式树,从而大大提高了挖掘效率。算法IUMFP充分利用以前的挖掘结果减少发现更新数据中新的最大频繁模式的代价。     

挖掘最大频繁模式的新方法

由于其内在的计算复杂性,挖掘密集型数据集的频繁模式完全集非常困难,解决方案之一是挖掘最大频繁模式集．该文在频繁模式完全集挖掘算法Opportune Project基础上,提出了挖掘最大频繁模式的新算法MOP．它采用宽度与深度优先相结合的混合搜索策略,能恰当地选择不同的支持集表示和投影方法,将闭合性剪裁和一般性剪裁相结合,并适时前窥,实现搜索与剪裁效率最优化．实验表明,MOP效率是MaxMiner的2～8倍,比MAFIA高2个数量级以上．     

基于RIA的视频语义标注系统研究与开发

文章主要以开发中文视频语义测试集为目的,提出了一种视频语义标注模型,分析了标注中使用的本体,提出了一种方便易用的标注方法,并以此为基础开发了视频语义标注系统。系统采用RIA技术进行开发,可以更好的进行信息的共享,通过对标注进行的反复检查、确保正确率。     

基于Top-K项频繁模式挖掘的研究及实现

频繁模式挖掘是关联规则、序列分析等数据挖掘任务的关键步骤,我们知道,当给定的最小支持度阈值非常小,将产生大量的频繁模式,反之,可能产生很少的模式或根本没有结果。用户有时仅对其中的部分项的频繁度感兴趣,这属于部分频繁模式挖掘问题。文章通过有效设置挖掘区间,讨论一种top—k项频繁模式挖掘问题,进而扩展到连续区间上的情况,最后将给出实验结果。     

一种改进的频繁模式挖掘算法

本文在分析研究FP-growth算法的基础上,提出了一种基于传统事务数据库下的频繁模式挖掘改进算法。实验证明该算法比FP-growth算法更有效,并具有较好的扩展性。     

分布环境中的并行频繁模式挖掘算法

频繁模式的并行挖掘算法是数据挖掘中重要的研究课题。目前已经提出的并行算法大多是基于Apriori或基于FP-tree。由于两者的固有局限性,而且在计算过程中需要多次同步,因而具有较低的性能。文章提出了一种基于分布数据库的并行挖掘算法。该算法尽可能地让每个处理器独立地挖掘,每个处理器基于前缀树采用深度优先搜索的策略挖掘局部频繁模式集,并通过相关性质尽量减少候选全局频繁模式的规模,减少网络的通信量和同步次数以提高挖掘效率。     

传感器网络中频繁移动模式挖掘算法研究

针对传感器网络中包括目标位置和时间的二维属性频繁移动模式挖掘问题,建立一种新的树状结构OMP-tree(OMP: Object Moving Pattern),OMP-tree可以压缩存储大量的原始移动模式.提出一种条件搜索算法,使用该算法可以大大减少满足条件的前缀模式数量.基于OMP-tree和条件搜索算法,设计一种新的挖掘目标的频繁移动模式的算法OMP-mine,该算法基于模式增长思想,直接递归地从条件模式基中得到频繁的前缀模式,然后连接后缀,达到模式增长的目的.仿真结果表明所提出的OMP-mine算法可以有效挖掘出传感器网络中具有二维属性的频繁的移动模式,并较好地降低了算法的时间和空间复杂度.     

A Pattern Decomposition Algorithm for Data Mining of Frequent Patterns

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass, making it more efficient to mine all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing the size of the dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree algorithm. Received 14 May 2001 / Revised 5 September 2001 / Accepted in revised form 26 October 2001 Correspondence and offprint requests to: Qinghua Zou, Department of Computer Science, California University–Los Angeles, CA 90095, USA. Email: zou@cs.ucla.eduau     

基于压缩FP-树和数组技术的频繁模式挖掘算法

FP-growth算法是目前较高效的频繁模式挖掘算法之一.它只需扫描数据库两次,而且不需要产生和测试候选集,避免了这些费时的工作,因此该算法具有较高的效率.然而,FP-growth算法需要递归地生成大量的条件FP-树,这耗费了大量的存储空间和时间.综合已有的几项优势技术,提出了一种频繁模式挖掘算法CFPmine. 一是采用了基于压缩FP-树的约束子树的挖掘方法,避免在挖掘过程中生成条件FP-树,减少内存占用;二是采用基于数组的技术,减少FP-树的遍历时间,提高算法的效率.另外,在算法中还实现了统一的内存管理.实验结果表明,CFPmine是一个高效的频繁模式挖掘算法,其性能优于Apriori,Eclat和FP-growth算法,而需要的内存却少于FP-growth算法.     

一种基于压缩前缀树的频繁模式挖掘算法

针对FP-growth算法存在动态维护复杂、在挖掘过程中需要递归地创建大量的条件频繁模式树,导致时空效率不高等不足,本算法在压缩前缀树的基础上,通过调整树中节点信息和节点链,采用深度优先的策略挖掘频繁模式,无需任何附加的数据结构,极大地减少了系统资源的消耗,减少树的规模和遍历次数,挖掘效率大大提高。     

一种基于新增阈值的频繁模式挖掘算法

在FP-growth算法的基础上,结合新的阈值,提出了一种改进的频繁模式树构造算法(NCFP-growth).该算法通过兴趣度权重的引入,有效地对频繁项做了进一步的过滤,从而减少了系统在采用FP-growth算法时所产生的大量冗余虚假的规则.对于FP-growth算法而言,该算法在构建频繁模式树时,有效地减小了树的规模,降低了系统存储空间,算法的搜索空间也得到了有效压缩.     

基于FP-tree的最大频繁模式挖掘算法

在FP-tree结构的基础上提出了最大频繁模式挖掘算法FP-Max。算法FP-Max只需要两次数据库扫描,挖掘过程不会产生候选项集。实验表明．算法FP-Max在挖掘密集型数据集方面是高效的。