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: Fusion in Computer Vision
    sub title: Understanding Complex Visual Content
  name:
    name part: Ionescu, Bogdan.
    role:
      role term: editor.
  name:
    name part: Benois-Pineau, Jenny.
    role:
      role term: editor.
  name:
    name part: Piatrik, Tomas.
    role:
      role term: editor.
  name:
    name part: Quénot, Georges.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 272 p. 74 illus., 65 illus. in color. online resource.
  abstract: Visual content understanding is a complex and important challenge for applications in automatic multimedia information indexing, medicine, robotics, and surveillance. Yet the performance of such systems can be improved by the fusion of individual modalities/techniques for content representation and machine learning. This comprehensive text/reference presents a thorough overview of Fusion in Computer Vision, from an interdisciplinary and multi-application viewpoint. Presenting contributions from an international selection of experts, the work describes numerous successful approaches, evaluated in the context of international benchmarks that model realistic use cases at significant scales. Topics and features: Examines late fusion approaches for concept recognition in images and videos, including the bag-of-words model Describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods Investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video Proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversity within the ensemble Reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies Discusses the modeling of mechanisms of human interpretation of complex visual content This authoritative collection is essential reading for researchers and students interested in the domain of information fusion for complex visual content understanding, and related fields.
  table of contents: A Selective Weighted Late Fusion for Visual Concept Recognition -- Bag-of-Words Image Representation: Key Ideas and Further Insight -- Hierarchical Late Fusion for Concept Detection in Videos -- Fusion of Multiple Visual Cues for Object Recognition in Video -- Evaluating Multimedia Features and Fusion for Example-Based Event Detection -- Rotation-Based Ensemble Classifiers for High Dimensional Data -- Multimodal Fusion in Surveillance Applications -- Multimodal Violence Detection in Hollywood Movies: State-of-the-Art and Benchmarking -- Fusion Techniques in Biomedical Information Retrieval -- Using Crowdsourcing to Capture Complexity in Human Interpretations of Multimedia Content.
  note: edited by Bogdan Ionescu, Jenny Benois-Pineau, Tomas Piatrik, Georges Quénot.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Multimedia information systems
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Computer Science
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Multimedia Information Systems
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  classification: TA1637-1638
  classification: TA1634
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Computer Vision and Pattern Recognition
  identifier: 9783319056968
  identifier: http://dx.doi.org/10.1007/978-3-319-05696-8
  record info:
    record creation date: 140325
    record change date: 20151030051129.0
    record identifier: 978-3-319-05696-8