As cameras become more pervasive in our daily life, vast amounts of video data are generated. The popularity of YouTube and similar websites such as Tudou and Youku provides strong evidence for the increasing role of video in society. One of the main challenges confronting us in the era of information technology is to - fectively rely on the huge and rapidly growing video data accumulating in large multimedia archives. Innovative video processing and analysis techniques will play an increasingly important role in resolving the difficult task of video search and retrieval. A wide range of video-based applications have benefited from - vances in video search and mining including multimedia information mana- ment, human-computer interaction, security and surveillance, copyright prot- tion, and personal entertainment, to name a few. This book provides an overview of emerging new approaches to video search and mining based on promising methods being developed in the computer vision and image analysis community. Video search and mining is a rapidly evolving discipline whose aim is to capture interesting patterns in video data. It has become one of the core areas in the data mining research community. In comparison to other types of data mining (e. g. text), video mining is still in its infancy. Many challenging research problems are facing video mining researchers.
With the explosion of video and image data available on the Internet, desktops and mobile devices, multimedia search has gained immense importance. Moreover, mining semantics and other useful information from large-scale multimedia data to facilitate online and local multimedia content analysis, search, and other related applications has also gained an increasing attention from the academia and industry. The rapid increase of multimedia data has brought new challenges to multimedia content analysis and multimedia retrieval, especially in terms of scalability. While on the other hand, large-scale multimedia data has also provided new opportunities to address these challenges and other conventional problems in multimedia analysis. The massive associated metadata, context and social information available on the Internet, desktops and mobile devices, and the large number of grassroots users, are a valuable resource that could be leveraged to solve the these difficulties. This is the first reference book on the subject of internet multimedia search and mining and it will be extremely useful for graduates, researchers and working professionals in the field of information technology and multimedia content analysis.
Multimedia Mining: A Highway to Intelligent Multimedia Documents brings together experts in digital media content analysis, state-of-art data mining and knowledge discovery in multimedia database systems, knowledge engineers and domain experts from diverse applied disciplines. Multimedia documents are ubiquitous and often required, if not essential, in many applications today. This phenomenon has made multimedia documents widespread and extremely large. There are tools for managing and searching within these collections, but the need for tools to extract hidden useful knowledge embedded within multimedia objects is becoming pressing and central for many decision-making applications. The tools needed today are tools for discovering relationships between objects or segments within multimedia document components, such as classifying images based on their content, extracting patterns in sound, categorizing speech and music, and recognizing and tracking objects in video streams.
Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed in recent years. The book first discusses the theoretical foundations of multimedia data mining, presenting commonly used feature representation, knowledge representation, statistical learning, and soft computing techniques. It then provides application examples that showcase the great potential of multimedia data mining technologies. In this part, the authors show how to develop a semantic repository training method and a concept discovery method in an imagery database. They demonstrate how knowledge discovery helps achieve the goal of imagery annotation. The authors also describe an effective solution to large-scale video search, along with an application of audio data classification and categorization. This novel, self-contained book examines how the merging of multimedia and data mining research can promote the understanding and advance the development of knowledge discovery in multimedia data.
First title to ever present soft computing approaches and theirapplication in data mining, along with the traditionalhard-computing approaches Addresses the principles of multimedia data compressiontechniques (for image, video, text) and their role in datamining Discusses principles and classical algorithms on stringmatching and their role in data mining
Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvi ous overlap with computer vision. The main goal of computer graphics is to generate and animate realistic looking images, and videos. Re searchers in computer graphics are increasingly employing techniques from computer vision to generate the synthetic imagery. A good exam pIe of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is derived from real images using computer vision techniques. Here the shift is from synthesis to analy sis followed by synthesis. Image processing has always overlapped with computer vision because they both inherently work directly with images.
The advent of increasingly large consumer collections of audio(e.g., iTunes), imagery (e.g., Flickr), and video (e.g., YouTube)is driving a need not only for multimedia retrieval but alsoinformation extraction from and across media. Furthermore,industrial and government collections fuel requirements for stockmedia access, media preservation, broadcast news retrieval,identity management, and video surveillance. Whilesignificant advances have been made in language processing forinformation extraction from unstructured multilingual text andextraction of objects from imagery and video, these advances havebeen explored in largely independent research communities who haveaddressed extracting information from single media (e.g., text,imagery, audio). And yet users need to search for conceptsacross individual media, author multimedia artifacts, and performmultimedia analysis in many domains. This collection is intended to serve several purposes, includingreporting the current state of the art, stimulating novel research,and encouraging cross-fertilization of distinct researchdisciplines. The collection and integration of a common base ofintellectual material will provide an invaluable service from whichto teach a future generation of cross disciplinary media scientistsand engineers.
This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing and visualization.
A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections. Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data. Addresses the area of multimedia retrieval and pays close attention to the issue of scalability Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios Includes tables, illustrations, and figures Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.
How could the peace and quiet of Ashe County, North Carolina (in the mountains, at the Virginia-Tennessee corner), turn into a nightmare of crime and drugs, and the old copper mine itself become a dumping ground for the dead? In 1982, two bodies had been chipped from an icy grave and brought up from the 250-foot mine shaft where they had been thrown while still alive. Now, there were rumors of 21 bodies still down there. If the mine was ever re-opened, what would they find—copper or bodies? Murder, drugs, prostitution and gangs come together in the history of the Ore Knob Mine. A small Appalachian community became the heart of a vicious drug ring ruled by the Outlaws motorcycle gang from Chicago. Ashe County made national headlines when a police informant came forward confessing that he had pushed a man alive into the Ore Knob Mine shaft. This book is the full story.
The evolution of technology has set the stage for the rapid growth of the video Web: broadband Internet access is ubiquitous, and streaming media protocols, systems, and encoding standards are mature. In addition to Web video delivery, users can easily contribute content captured on low cost camera phones and other consumer products. The media and entertainment industry no longer views these developments as a threat to their established business practices, but as an opportunity to provide services for more viewers in a wider range of consumption contexts. The emergence of IPTV and mobile video services offers unprecedented access to an ever growing number of broadcast channels and provides the flexibility to deliver new, more personalized video services. Highly capable portable media players allow us to take this personalized content with us, and to consume it even in places where the network does not reach. Video search engines enable users to take advantage of these emerging video resources for a wide variety of applications including entertainment, education and communications. However, the task of information extr- tion from video for retrieval applications is challenging, providing opp- tunities for innovation. This book aims to first describe the current state of video search engine technology and second to inform those with the req- site technical skills of the opportunities to contribute to the development of this field. Today’s Web search engines have greatly improved the accessibility and therefore the value of the Web.
This volume provides an overview of multimedia data mining and knowledge discovery and discusses the variety of hot topics in multimedia data mining research. It describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field.
This volume contains the proceeding of the 5th International Conference on - age and Video Retrieval (CIVR), July 13–15, 2006, Arizona State University, Tempe, AZ, USA: http://www. civr2006. org. Image and video retrieval cont- ues to be one of the most exciting and fast-growing research areas in the ?eld of multimedia technology. However, opportunities for exchanging ideas between researchers and users of image and video retrieval systems are still limited. The InternationalConferenceonImageandVideo Retrieval(CIVR)hastakenonthe mission of bringing together these communities to allow researchers and prac- tioners around the world to share points of view on image and video retrieval. A uniquefeatureoftheconferenceistheemphasisonparticipationfrompractiti- ers. The objective is to illuminate critical issues and energize both communities for the continuing exploration of novel directions for image and video retrieval. We receivedover 90 submissions for the conference. Eachpaper wascarefully reviewed by three members of the program committee, and then checked by one of the program chairs and/or general chairs. The program committee consisted of more than 40 experts in image and video retrieval from Europe, Asia and North America, and we drew upon approximately 300 high-quality reviews to ensure a thorough and fair review process. The paper submission and review process was fully electronic, using the EDAS system. The quality of the submitted papers was very high, forcing the committee members to make some di?cult decisions. Due to time and space constraints, we could only accept 18 oral papers and 30 poster papers.
Digital Image Computing: Techniques and Applications is the premier biennial conference in Australia on the topics of image processing and image analysis. This seventh edition of the proceedings has seen an unprecedented level of submission, on such diverse areas as: Image processing; Face recognition; Segmentation; Registration; Motion analysis; Medical imaging; Object recognition; Virtual environments; Graphics; Stereo-vision; and Video analysis. These two volumes contain all the 108 accepted papers and five invited talks that were presented at the conference. These two volumes provide the Australian and international imaging research community with a snapshot of current theoretical and practical developments in these areas. They are of value to any engineer, computer scientist, mathematician, statistician or student interested in these matters.
In the 1920s there were over a million coalminers working in over 3000 collieries across Great Britain, and the industry was one of the most important and powerful in British history. It dominated the lives of generations of individuals, their families and communities, and its legacy is still with us today many of us have a coalmining ancestor. Yet family historians often have problems in researching their mining forebears. Locating the relevant records, finding the sites of the pits, and understanding the work involved and its historical background can be perplexing. That is why Brian Elliott's concise, authoritative and practical handbook will be so useful, for it guides researchers through these obstacles and opens up the broad range of sources they can go to in order to get a vivid insight into the lives and experiences of coalminers in the past. His overview of the coalmining history and the case studies and research tips he provides will make his book rewarding reading for anyone looking for a general introduction to this major aspect of Britain's industrial heritage. His directory of regional and national sources and his commentary on them will make this guide an essential tool for family historians searching for an ancestor who worked in coalmining underground, on the pit top or just lived in a mining community.As featured in Who Do You Think You Are? Magazine and the Barnsley Chronicle.
This book explores multimedia applications that emerged from computer vision and machine learning technologies. These state-of-the-art applications include MPEG-7, interactive multimedia retrieval, multimodal fusion, annotation, and database re-ranking. The application-oriented approach maximizes reader understanding of this complex field. Established researchers explain the latest developments in multimedia database technology and offer a glimpse of future technologies. The authors emphasize the crucial role of innovation, inspiring users to develop new applications in multimedia technologies such as mobile media, large scale image and video databases, news video and film, forensic image databases and gesture databases. With a strong focus on industrial applications along with an overview of research topics, Multimedia Database Retrieval: Technology and Applications is an indispensable guide for computer scientists, engineers and practitioners involved in the development and use of multimedia systems. It also serves as a secondary text or reference for advanced-level students interested in multimedia technologies.
This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.
This book constitutes the refereed proceedings of the First International Conference on Soft Computing and Data Mining, SCDM 2014, held in Universiti Tun Hussein Onn Malaysia, in June 16th-18th, 2014. The 65 revised full papers presented in this book were carefully reviewed and selected from 145 submissions, and organized into two main topical sections; Data Mining and Soft Computing. The goal of this book is to provide both theoretical concepts and, especially, practical techniques on these exciting fields of soft computing and data mining, ready to be applied in real-world applications. The exchanges of views pertaining future research directions to be taken in this field and the resultant dissemination of the latest research findings makes this work of immense value to all those having an interest in the topics covered.
Data mining brings together techniques from machine learning, pattern recognition, statistics, databases, linguistics and visualization in order to extract information from large databases. Originally principally concerned with behavioural applications, such as the understanding of customer behaviour, its scope has now been widened with the introduction of Text Mining techniques. Areas now encompassed by data mining include military, market, and competitive intelligence applications, taxonomies and internet search techniques, and knowledge management applications.