Deep Learning for Multimedia Processing Applications
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| ISBN13: | 9781032548241 |
| ISBN10: | 103254824X |
| Binding: | Hardback |
| No. of pages: | 312 pages |
| Size: | 254x178 mm |
| Weight: | 740 g |
| Language: | English |
| Illustrations: | 53 Illustrations, black & white; 62 Illustrations, color; 41 Halftones, black & white; 26 Halftones, color; 12 Line drawings, black & white; 36 Line drawings, color; 75 Tables, black & white |
| 689 |
Electrical engineering and telecommunications, precision engineering
Energy industry
Theory of computing, computing in general
Operating systems and graphical user interfaces
Computer programming in general
Digital signal, audio and image processing
Safety and health aspects of computing
Other integrated software packages
Environmental sciences
Internet services (online shopping, banking)
This book is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains.
Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.
Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.
Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.
Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
1. A Novel Robust Watermarking Algorithm for Encrypted Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition Meng Yang, Jingbing Li1, Uzair Aslam Bhatti, Yiyi Yuan, and QinQing Zhang. 2. Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on SUSAN-DCT Jingbing Li, Qinqing Zhang, Meng Yang, and Yiyi Yuan. 3. Robust Watermarking Algorithm for Encrypted Medical Volume Data Based on PJFM and 3D-DCT Lei Cao, Jingbing Li, and Uzair Aslam Bhatti. 4. Robust Zero Watermarking Algorithm for Medical Images Based on BRISK and DCT Fangchun Dong, Jingbing Li, and Uzair Aslam Bhatt. 5. Robust Color Images Zero Watermarking Algorithm Based on Smooth Wavelet Transform and Daisy descriptor Yiyi Yuan, Jingbing Li1, Uzair Aslam Bhatti, Meng Yang, and Qinqing Zhang. 6. Robust Multi-Watermarking Agorithm based on Darknet53 Convolutional Neural Network Dekai Li, Jingbing Li, and Uzair Aslam Bhatti. 7. Robust Multi-Watermarking Algorithm for Medical Images Based on Squeezenet Transfer Learning Pengju Zhang, Jingbing Li, and Uzair Aslam Bhatti. 8. Deep Learning Applications in Digital Image Security: Latest Methods And Techniques Saqib Ali Nawaz, Jingbing Li, Uzair Aslam Bhatti, Muhammad Usman Shoukat, and Raza Muhammad Ahmad. 9. Image Fusion Techniques and Applications for Remote Sensing Images and Medical Images Emadalden Alhatami, MengXing Huang, and Uzair Aslam Bhatti. 10. Detecting Phishing URLs Through Deep Learning Models Shah Noor, Sibghat Ullah Bazai, Saima Tareen, and Shafi Ullah. 11. Augmenting Multimedia Analysis: A Fusion of Deep Learning with Differential Privacy Iqra Tabassum and Dr. Sibghat Ullah Bazai. 12. Multi-Classification Deep Learning Models for Detecting Multiple Chest Infection using Cough and Breath Sound Amna Tahir, Hassaan Malik, and Muhammad Umar Chaudhry. 13. Classifying Traffic Signs using Convolutional Neural Networks based on Deep Learning Models Saira Akram, Sibghat Ullah Bazai, and Shah Marjan. 14. Cloud-Based Intrusion Detection System using Deep Neural Network and Human-in-the-Loop Decision-Making Hootan Alavizadeh and Hooman Alavizadeh.