Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing) [Hardcover]

Francesco Camastra (Author), Alessandro Vinciarelli (Author)

Book Description

ISBN-10: 1848000065 | ISBN-13: 978-1848000063 | Publication Date: December 4, 2007 | Edition: 1st Edition.

Focusing on complex media and how to convert raw data into useful information, this book offers both introductory and advanced material in the combined fields of machine learning and image/video processing. It is organized into three parts. The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing. The third focuses on applications and shows how techniques are applied in actual problems. Examples and problems are based on data and software packages publicly available on the web.

Editorial Reviews

Review

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)

From the Back Cover

Machine Learning involves several scientific domains including mathematics, computer science, statistics and biology, and is an approach that enables computers to automatically learn from data. Focusing on complex media and how to convert raw data into useful information, this book offers both introductory and advanced material in the combined fields of machine learning and image/video processing. The machine learning techniques presented enable readers to address many real world problems involving complex data. Examples covering areas such as automatic speech and handwriting transcription, automatic face recognition, and semantic video segmentation are included, along with detailed introductions to algorithms and examples of their applications. The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text. Students and researchers needing a solid foundation or reference, and practitioners interested in discovering more about the state-of-the-art will find this book invaluable. Examples and problems are based on data and software packages publicly available on the web.

Product Details

• Hardcover: 512 pages
• Publisher: Springer; 1st Edition. edition (December 4, 2007)
• Language: English
• ISBN-10: 1848000065
• ISBN-13: 978-1848000063
• Product Dimensions: 9.2 x 6.3 x 1.4 inches
• Shipping Weight: 1.8 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,713,731 in Books (See Top 100 in Books)
  • #40 in Books > Computers & Technology > Digital Music > MP3

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Customer Reviews

Most Helpful Customer Reviews

2 of 2 people found the following review helpful:

4.0 out of 5 stars A pretty good effort for a complex topic, March 6, 2008

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calvinnme - See all my reviews
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This review is from: Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing) (Hardcover)

This book is divided into three parts:

From Perception to Computation - Shows how the physical supports our auditory and visual perceptions. In other words, it shows how acoustic waves and electromagnetic radiation are converted into objects that can be manipulated by a computer.

Machine Learning - Provides a rather deep survey of the main techniques used in machine learning. These chapters cover most of the algorithms applied in systems for audio, image, and video analysis. At this point, all of the algorithms are general pattern recognition techniques that could apply to any field.

Applications - This section presents examples of applications using the techniques presented in part two. There is a chapter each dedicated to speech and handwriting recognition, face recognition, and video segmentation and keyframe extraction. Each chapter shows an overall system where analysis and machine learning components interact in order to accomplish a given task. Whenever possible the chapters of this part present results obtained using publicly available data and software package. This enables the reader to perform experiments similar to those presented in this book.

The beginning of each chapter starts with what the reader should understand before getting started, such as calculus and chapter four in the case of chapter eleven. That is followed with a list of what the reaer should know after reading the chapter. I'd say parts one and two are quite good, but things break down a bit in part three. Granted, the subject of each of the three chapters in the final section is complex, but a few more figures and labeled algorithmic steps and maybe a little less prose might have made the specific matters of each task at hand clearer.