Brief Description
This book illustrates how to deal with complex media and convert raw data into useful information. 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.
Learn More about the Book
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.
On the Back Cover
Thissecond edition focuses on audio, image and video data, thethree main types of input that machines deal with wheninteracting with the real world. A set of appendices providesthe reader with self-containedintroductions to the mathematical background necessary to read the book.
Divided into three main parts, "From Perception to Computation" introducesmethodologies aimed at representingthe data in forms suitable for computer processing, especially when it comes to audioand images. Whilst thesecond part, "Machine Learning" includes an extensive overview of statistical techniques aimed at addressingthree main problems, namely classification (automatically assigning a data sample to one of the classesbelonging to apredefined set), clustering (automatically grouping data samples according to the similarityof their properties) and sequenceanalysis (automatically mapping a sequence of observations into a sequenceof human-understandable symbols). The thirdpart "Applications" shows how the abstract problems definedin the second part underlie technologies capable to performcomplex tasks such as the recognition of handgestures 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 practitionersto deepentheir knowledge of the state-of-the-art. All application chapters are based on publicly availabledata and free softwarepackages, thus allowing readers to replicate the experiments.
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Review Quotes
1. 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)
2.
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)"
3.
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)"
