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Titlebook: Deep Learning in Mining of Visual Content; Akka Zemmari,Jenny Benois-Pineau Book 2020 The Author(s), under exclusive license to Springer N

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发表于 2025-3-21 17:00:35 | 显示全部楼层 |阅读模式
书目名称Deep Learning in Mining of Visual Content
编辑Akka Zemmari,Jenny Benois-Pineau
视频video
概述A comprehensive overview of winning methods in visual content mining.Illustration of main concepts with graphical examples.Tracing analogy with classical visual content analysis tools
丛书名称SpringerBriefs in Computer Science
图书封面Titlebook: Deep Learning in Mining of Visual Content;  Akka Zemmari,Jenny Benois-Pineau Book 2020 The Author(s), under exclusive license to Springer N
描述This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. .It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. .Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to comp
出版日期Book 2020
关键词Artificial Intelligence; Supervised Machine Learning; Deep Learning; Artificial Neural Networks; Convolu
版次1
doihttps://doi.org/10.1007/978-3-030-34376-7
isbn_softcover978-3-030-34375-0
isbn_ebook978-3-030-34376-7Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2020
The information of publication is updating

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978-3-030-34375-0The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
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Aidan Beggs,Alexandros Kapravelosd dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with these operations in a classical Image Processing framework.
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https://doi.org/10.1007/978-3-030-22038-9der those designed for particular data: images. First of all we will expose some general principles, then go into detail layer-by-layer and finally briefly overview most popular convolutional neural networks architectures.
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Michael Brengel,Christian Rossowg consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms of space partition. Probably, the most popular of such grouping algorithms in visual content mining is the K-means approach introduced by MacQueen as ear
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