Machine Learning for Audio, Image and Video Analysis

Theory and Applications

Francesco Camastra, Alessandro Vinciarelli

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English

Springer London Ltd
03 August 2015

Graphical & digital media applications; Pattern recognition; Computer vision

Series: Advanced Information and Knowledge Processing

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 partApplications 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.

By: Francesco Camastra, Alessandro Vinciarelli
Imprint: Springer London Ltd
Country of Publication: United Kingdom
Edition: 2nd ed. 2015
Dimensions: Height: 235mm, Width: 155mm, Spine: 32mm
Weight: 9.871kg
ISBN: 9781447167341
ISBN 10: 1447167341
Series: Advanced Information and Knowledge Processing
Pages: 561
Publication Date: 03 August 2015
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

Introduction.- Part I: From Perception to Computation.- Audio Acquisition, Representation and Storage.- Image and Video Acquisition, Representation and Storage.- Part II: Machine Learning.- Machine Learning.- Bayesian Theory of Decision.- Clustering Methods.- Foundations of Statistical Learning and Model Selection.- Supervised Neural Networks and Ensemble Methods.- Kernel Methods.- Markovian Models for Sequential Data.- Feature Extraction Methods and Manifold Learning Methods.- Part III: Applications.- Speech and Handwriting Recognition.- Speech and Handwriting Recognition.- Video Segmentation and Keyframe Extraction.- Real-Time Hand Pose Recognition.- Automatic Personality Perception.- Part IV: Appendices.- Appendix A: Statistics.- Appendix B: Signal Processing.- Appendix C: Matrix Algebra.- Appendix D: Mathematical Foundations of Kernel Methods.- Index.

Reviews for Machine Learning for Audio, Image and Video Analysis: Theory and Applications

From the reviews: A book that focuses on the intersection and intersection of these two fast-growing areas could not be better timed. the book is organized into three major parts that cover audio and video processing, machine learning, and applications. On the whole, this is a valuable and timely reference book for those interested in machine learning or audio, video, and image processing, although the need for a well-integrated book on this topic still remains. (M. Sasikumar, ACM Computing Reviews, December, 2008) this book, unlike most other books in this field, not only introduces a few widely used techniques in audio and image analysis, but also discusses the latest advancements in the field. Distinct from other books, it also points out several public software packages and benchmark data sets that encourage the reader to have a hands-on experience on how machine-learning techniques work to analyze audio and visual content. Its comprehensive coverage on recent development in this research area makes it easy for experienced researchers to further explore the latest techniques. it is ideal as a textbook or supplemental material for senior graduate courses or advanced topic seminars. (Jie Yu, Journal of Electronic Imaging, Vol. 18, Apr Jun 2009)