rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning for Audio, Image and Video Analysis
    sub title: Theory and Applications
  name:
    name part: Camastra, Francesco.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Vinciarelli, Alessandro.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2015
    edition: 2nd ed. 2015.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 561 p. 119 illus. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Francesco Camastra, Alessandro Vinciarelli.
  subject:
    topic: Computer science
  subject:
    topic: Multimedia information systems
  subject:
    topic: Image processing
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Pattern Recognition
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Multimedia Information Systems
  classification: Q337.5
  classification: TK7882.P3
  classification: 006.4
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advanced Information and Knowledge Processing
  identifier: 9781447167358
  identifier: http://dx.doi.org/10.1007/978-1-4471-6735-8
  record info:
    record creation date: 150721
    record change date: 20151030101313.0
    record identifier: 978-1-4471-6735-8