Machine Learning For Audio Image And Video Analysis

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

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 Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. 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.

This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

This volume introduces machine learning techniques that are particularly powerful and effective for modeling multimedia data and common tasks of multimedia content analysis. It systematically covers key machine learning techniques in an intuitive fashion and demonstrates their applications through case studies. Coverage includes examples of unsupervised learning, generative models and discriminative models. In addition, the book examines Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM).

Computational Vision and Medical Image Processing. VIPIMAGE 2013 contains invited lectures and full papers presented at VIPIMAGE 2013 - IV ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (Funchal, Madeira Island, Portugal, 14-16 October 2013). International contributions from 16 countries provide a comprehensive coverage of the current state-of-the-art in the fields of: 3D Vision; Computational Bioimaging and Visualization; Computational Vision and Image Processing applied to Dental Medicine; Computational Vision; Computer Aided Diagnosis, Surgery, Therapy, and Treatment; Data Interpolation, Registration, Acquisition and Compression; Image Processing and Analysis; Image Segmentation; Imaging of Biological Flows; Medical Imaging; Physics of Medical Imaging; Shape Reconstruction; Signal Processing; Simulation and Modeling; Software Development for Image Processing and Analysis; Telemedicine Systems and their Applications; Trabecular Bone Characterization; Tracking and Analysis of Movement; Virtual Reality. Related techniques covered in this book include the level set method, finite element method, modal analyses, stochastic methods, principal and independent components analysis and distribution models. Computational Vision and Medical Image Processing. VIPIMAGE 2013 is useful to academics, researchers and professionals in Biomechanics, Biomedical Engineering, Computational Vision (image processing and analysis), Computer Sciences, Computational Mechanics and Medicine.

As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.

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.