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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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Multimedia data mining --- Conference papers and proceedings. --- Pattern recognition systems. --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Media mining (Data mining) --- Mining multimedia (Data mining) --- Multimedia mining (Data mining) --- Content-based image retrieval --- Data mining

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Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time. Yet the wide-scale automation of the news itself has largely escaped attention. In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories--increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism. Newsbots converse with social media audiences, distributing stories and receiving feedback. Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences. Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate. But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation. Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced. With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news. The human-algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.--

Journalism --- Online journalism. --- Digital media. --- Algorithms. --- Multimedia data mining. --- Media mining (Data mining) --- Mining multimedia (Data mining) --- Multimedia mining (Data mining) --- Content-based image retrieval --- Data mining --- Algorism --- Algebra --- Arithmetic --- Electronic media --- New media (Digital media) --- Mass media --- Digital communications --- Online journalism --- Electronic journalism --- Internet journalism --- Digital media --- Technological innovations. --- Foundations --- Artificial intelligence. Robotics. Simulation. Graphics --- Algorithms --- Multimedia data mining --- Technological innovations

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Amid the push for self-driving cars and the roboticization of industrial economies, automation has proven one of the biggest news stories of our time. Yet the wide-scale automation of the news itself has largely escaped attention. In this lively exposé of that rapidly shifting terrain, Nicholas Diakopoulos focuses on the people who tell the stories--increasingly with the help of computer algorithms that are fundamentally changing the creation, dissemination, and reception of the news. Diakopoulos reveals how machine learning and data mining have transformed investigative journalism. Newsbots converse with social media audiences, distributing stories and receiving feedback. Online media has become a platform for A/B testing of content, helping journalists to better understand what moves audiences. Algorithms can even draft certain kinds of stories. These techniques enable media organizations to take advantage of experiments and economies of scale, enhancing the sustainability of the fourth estate. But they also place pressure on editorial decision-making, because they allow journalists to produce more stories, sometimes better ones, but rarely both. Automating the News responds to hype and fears surrounding journalistic algorithms by exploring the human influence embedded in automation. Though the effects of automation are deep, Diakopoulos shows that journalists are at little risk of being displaced. With algorithms at their fingertips, they may work differently and tell different stories than they otherwise would, but their values remain the driving force behind the news. The human-algorithm hybrid thus emerges as the latest embodiment of an age-old tension between commercial imperatives and journalistic principles.--

Journalism --- Online journalism --- Digital media --- Algorithms --- Multimedia data mining --- Technological innovations --- Algorithms. --- Digital media. --- Multimedia data mining. --- Online journalism. --- Technological innovations. --- Artificial intelligence. Robotics. Simulation. Graphics --- #SBIB:309H1730 --- #SBIB:309H1010 --- #SBIB:309H301 --- Media mining (Data mining) --- Mining multimedia (Data mining) --- Multimedia mining (Data mining) --- Content-based image retrieval --- Data mining --- Algorism --- Algebra --- Arithmetic --- Electronic media --- New media (Digital media) --- Mass media --- Digital communications --- Electronic journalism --- Internet journalism --- Artificiële Intelligentie, knowledge engineering, .. --- Organisatorische aspecten van de media: algemene werken (incl. journalistiek) --- De communicator in de verschillende media (pers, omroep, film, boekenindustrie, ...) --- Foundations --- Datamining en nieuwsberichtgeving --- Datajournalistiek --- Artificiële Intelligentie, knowledge engineering, . --- Artificiële Intelligentie, knowledge engineering, --- Journalism - Technological innovations

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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