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Multimedia data mining --- Pattern recognition systems --- Media mining (Data mining) --- Mining multimedia (Data mining) --- Multimedia mining (Data mining) --- Content-based image retrieval --- Data mining

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Who could be partners to archivists working in digital preservation? This book features chapters from international contributors from diverse backgrounds and professions discussing their challenges with and victories over digital problems that share common issues with those facing digital preservationists The only certainty about technology is that it will change. The speed of that change, and the ever increasing diversity of digital formats, tools, and platforms, will present stark challenges to the long-term preservation of digital records. Archivists are frequently challenged by the technical expertise, subject matter knowledge, time, and resource requirements needed to solve the broad set of challenges sure to be faced by the archival profession. Partners for Preservation advocates the need for archivists to recruit partners and learn lessons from across diverse professions to work more effectively within the digital landscape. Includes discussion of: the internet of things digital architecture research data and collaboration open source programming privacy, memory and transparency inheritance of digital media. This book will be useful reading for professional archivists and others responsible for digital preservation, students of archival studies and digital preservation

Archival materials --- Museum conservation methods --- Digital preservation. --- Multimedia data mining. --- Digitization. --- Data processing. --- Media mining (Data mining) --- Mining multimedia (Data mining) --- Multimedia mining (Data mining) --- Content-based image retrieval --- Data mining --- Computer files --- Digital curation --- Digital media --- Electronic preservation --- Preservation of digital information --- Preservation of materials --- Conservation methods, Museum --- Museum techniques --- Digitalization of archival materials --- Digitization of archival materials --- Conservation and restoration --- Preservation --- 930.25:681.3 --- 930.25:681.3 Archiefwetenschap. Archivistiek-:-Computerwetenschap --- Archiefwetenschap. Archivistiek-:-Computerwetenschap

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This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining Emphasizes how to deal with real image data for practical image mining Highlights how such features as color, texture, and shape can be mined or extracted from images for image representation Presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees Discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods Provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter This easy-to-follow work illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing. Dr. Dengsheng Zhang is a Senior Lecturer in the School of Science, Engineering and Information Technology at Federation University Australia.

Computer vision. --- Data mining. --- Machine learning. --- Engineering mathematics. --- Big data. --- Image Processing and Computer Vision. --- Data Mining and Knowledge Discovery. --- Machine Learning. --- Engineering Mathematics. --- Big Data. --- Data sets, Large --- Large data sets --- Data sets --- Engineering --- Engineering analysis --- Mathematical analysis --- Learning, Machine --- Artificial intelligence --- Machine theory --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Mathematics --- Multimedia data mining. --- Media mining (Data mining) --- Mining multimedia (Data mining) --- Multimedia mining (Data mining) --- Content-based image retrieval --- Data mining --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment