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    Multifeature analysis and semantic context learning for image classification 
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    Multifeature analysis and semantic context learning for image classification

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    Accepted Version (2.788Mb)
    Volume
    2
    Publisher
    ACM New York, NY, USA
    Journal
    ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
    Issue
    9
    Metadata
    Show full item record
    Abstract
    This article introduces an image classification approach in which the semantic context of images and multiple low-level visual features are jointly exploited. The context consists of a set of semantic terms defining the classes to be associated to unclassified images. Initially, a multiobjective optimization technique is used to define a multifeature fusion model for each semantic class. Then, a Bayesian learning procedure is applied to derive a context model representing relationships among semantic classes. Finally, this ...
    Authors
    ZHANG, Q; Izquierdo, E
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/13512
    Collections
    • Computer Vision Group [43]
    Licence information
    “The final publication is available at http://dl.acm.org/citation.cfm?id=2457454”
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