Multimodal Video Characterization and Summarization is a valuable research tool for both professionals and academicians working in the video field. This book describes the methodology for using multimodal audio, image, and text technology to characterize video content. This new and groundbreaking science has led to many advances in video understanding, such as the development of a video summary. Applications and methodology for creating video summaries are described, as well as user-studies for evaluation and testing.
This book presents a systematic introduction to the latest developments in video text detection. Opening with a discussion of the underlying theory and a brief history of video text detection, the text proceeds to cover pre-processing and post-processing techniques, character segmentation and recognition, identification of non-English scripts, techniques for multi-modal analysis and performance evaluation. The detection of text from both natural video scenes and artificially inserted captions is examined. Various applications of the technology are also reviewed, from license plate recognition and road navigation assistance, to sports analysis and video advertising systems. Features: explains the fundamental theory in a succinct manner, supplemented with references for further reading; highlights practical techniques to help the reader understand and develop their own video text detection systems and applications; serves as an easy-to-navigate reference, presenting the material in self-contained chapters.
This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent research.
Technology has spurred the growth of huge image and video libraries, many growing into the hundreds of terabytes. As a result there is a great demand among organizations for the design of databases that can effectively support the storage, search, retrieval, and transmission of video data. Engineers and researchers in the field demand a comprehensi
This volume presents high quality, state-of-the-art research ideas and results from theoretic, algorithmic and application viewpoints. It contains contributions by leading experts in the obsequious scientific and technological field of multimedia. The book specifically focuses on interaction with multimedia content with special emphasis on multimodal interfaces for accessing multimedia information. The book is designed for a professional audience composed of practitioners and researchers in industry. It is also suitable for advanced-level students in computer science.
Advances in hardware, software, and audiovisual rendering technologies of recent years have unleashed a wealth of new capabilities and possibilities for multimedia applications, creating a need for a comprehensive, up-to-date reference. The Encyclopedia of Multimedia Technology and Networking provides hundreds of contributions from over 200 distinguished international experts, covering the most important issues, concepts, trends, and technologies in multimedia technology. This must-have reference contains over 1,300 terms, definitions, and concepts, providing the deepest level of understanding of the field of multimedia technology and networking for academicians, researchers, and professionals worldwide.
This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.
Volume II This collection brings together work on the relevance of Wittgenstein’s philosophy to the field of Artificial Intelligence (AI). Over two volumes, our contributors cover a wide range of topics from different disciplinary approaches. In this Volume (II), contributions are centred on two major themes in the philosophy of AI: questions of value and governance. Contributions include chapters on both ethics and aesthetics and AI, as well as questions of the governance of AI systems, including legal and policy issues.
Electronic engineering and informatics are disciplines which underpin the complex digital technology on which we have all now come to depend. This book presents the proceedings of ICEEI 2023, the 5th International Conference on Electronic Engineering and Informatics, which took place as a hybrid event from 23 to 25 June 2023 in Wuhan, China, with around 150 participating delegates. The conference brought together leading academics, researchers and practitioners from around the world to present recent innovations, trends, and concerns, and discuss practical challenges and solutions. It also gave delegates the opportunity to share their experience and research results and exchange views on all aspects of electronic engineering and informatics. A total of 266 submissions were received for the conference, of which 93 were accepted for presentation and publication after a careful double-blind peer review process. The papers are divided into 3 sections, covering electronic device simulation and system modelling; target recognition and information decision making; and network data processing and security detection. Providing a current overview of advances and research results in the relevant fields, the book will be of interest to those working in all areas of electronic engineering and informatics.
The two-volume set LNCS 10761 + 10762 constitutes revised selected papers from the CICLing 2017 conference which took place in Budapest, Hungary, in April 2017. The total of 90 papers presented in the two volumes was carefully reviewed and selected from numerous submissions. In addition, the proceedings contain 4 invited papers. The papers are organized in the following topical sections: Part I: general; morphology and text segmentation; syntax and parsing; word sense disambiguation; reference and coreference resolution; named entity recognition; semantics and text similarity; information extraction; speech recognition; applications to linguistics and the humanities. Part II: sentiment analysis; opinion mining; author profiling and authorship attribution; social network analysis; machine translation; text summarization; information retrieval and text classification; practical applications.
