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 Techniques for Multimedia
    sub title: Case Studies on Organization and Retrieval
  name:
    name part: Cord, Matthieu.
    role:
      role term: editor.
  name:
    name part: Cunningham, Pádraig.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 289 p. online resource.
  abstract: Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Applying machine learning techniques to multimedia content involves special considerations – the data is typically of very high dimension, and the normal distinction between supervised and unsupervised techniques does not always apply. This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains such as image retrieval, biometrics, semantic labelling, mobile devices, and mining in text and music. This book will be suitable for practitioners, researchers and students engaged with machine learning in multimedia applications.
  table of contents: to Learning Principles for Multimedia Data -- to Bayesian Methods and Decision Theory -- Supervised Learning -- Unsupervised Learning and Clustering -- Dimension Reduction -- Multimedia Applications -- Online Content-Based Image Retrieval Using Active Learning -- Conservative Learning for Object Detectors -- Machine Learning Techniques for Face Analysis -- Mental Search in Image Databases: Implicit Versus Explicit Content Query -- Combining Textual and Visual Information for Semantic Labeling of Images and Videos -- Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization -- Classification and Clustering of Music for Novel Music Access Applications.
  note: edited by Matthieu Cord, Pádraig Cunningham.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: User interfaces (Computer systems)
  subject:
    topic: Artificial intelligence
  subject:
    topic: Text processing (Computer science)
  subject:
    topic: Computer graphics
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Information Storage and Retrieval
  subject:
    topic: User Interfaces and Human Computer Interaction
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Document Preparation and Text Processing
  subject:
    topic: Computer Imaging, Vision, Pattern Recognition and Graphics
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Cognitive Technologies
  identifier: 9783540751717
  identifier: http://dx.doi.org/10.1007/978-3-540-75171-7
  record info:
    record creation date: 100301
    record change date: 20151125192054.0
    record identifier: 978-3-540-75171-7