Video Search and Mining


Book Description

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.




Internet Multimedia Search and Mining


Book Description

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.




Video Mining


Book Description

Video Mining is an essential reference for the practitioners and academicians in the fields of multimedia search engines. Half a terabyte or 9,000 hours of motion pictures are produced around the world every year. Furthermore, 3,000 television stations broadcasting for twenty-four hours a day produce eight million hours per year, amounting to 24,000 terabytes of data. Although some of the data is labeled at the time of production, an enormous portion remains unindexed. For practical access to such huge amounts of data, there is a great need to develop efficient tools for browsing and retrieving content of interest, so that producers and end users can quickly locate specific video sequences in this ocean of audio-visual data. Video Mining is important because it describes the main techniques being developed by the major players in industry and academic research to address this problem. It is the first time research from these leaders in the field developing the next-generation multimedia search engines is being described in great detail and gathered into a single volume. Video Mining will give valuable insights to all researchers and non-specialists who want to understand the principles applied by the multimedia search engines that are about to be deployed on the Internet, in studios' multimedia asset management systems, and in video-on-demand systems.




Advances in Multimedia Information Processing – PCM 2017


Book Description

The two-volume set LNCS 10735 and 10736 constitutes the thoroughly refereed proceedings of the 18th Pacific-Rim Conference on Multimedia, PCM 2017, held in Harbin, China, in September 2017. The 184 full papers presented were carefully reviewed and selected from 264 submissions. The papers are organized in topical sections on: Best Paper Candidate; Video Coding; Image Super-resolution, Debluring, and Dehazing; Person Identity and Emotion; Tracking and Action Recognition; Detection and Classification; Multimedia Signal Reconstruction and Recovery; Text and Line Detection/Recognition; Social Media; 3D and Panoramic Vision; Deep Learning for Signal Processing and Understanding; Large-Scale Multimedia Affective Computing; Sensor-enhanced Multimedia Systems; Content Analysis; Coding, Compression, Transmission, and Processing.




Big Data Analytics for Large-Scale Multimedia Search


Book Description

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.




Big Data Analytics for Large-Scale Multimedia Search


Book Description

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.




Managing and Mining Graph Data


Book Description

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.




Multimedia Information Extraction


Book Description

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 also information extraction from and across media. Furthermore, industrial and government collections fuel requirements for stock media access, media preservation, broadcast news retrieval, identity management, and video surveillance. While significant advances have been made in language processing for information extraction from unstructured multilingual text and extraction of objects from imagery and video, these advances have been explored in largely independent research communities who have addressed extracting information from single media (e.g., text, imagery, audio). And yet users need to search for concepts across individual media, author multimedia artifacts, and perform multimedia analysis in many domains. This collection is intended to serve several purposes, including reporting the current state of the art, stimulating novel research, and encouraging cross-fertilization of distinct research disciplines. The collection and integration of a common base of intellectual material will provide an invaluable service from which to teach a future generation of cross disciplinary media scientists and engineers.




Image and Video Retrieval


Book Description

This book constitutes the refereed proceedings of the Third International Conference on Image and Video Retrieval, CIVR 2004, held in Dublin, Ireland in July 2004. The 31 revised full papers and 44 poster papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on image annotation and user searching, image and video retrieval algorithms, person and event identification for retrieval, content-based image and video retrieval, and user perspectives.




Computer Vision – ECCV 2020


Book Description

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.