Combining Image-Level and Segment-Level Models for Automatic Annotation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Daniel Kuettel , Matthieu Guillaumin , Vittorio Ferrari

ABSTRACT

For the task of assigning labels to an image to summarize its contents, many early attempts use segment-level information and try to determine which parts of the images correspond to which labels. Best performing methods use global image similarity and nearest neighbor techniques to transfer labels from training images to test images. However, global methods cannot localize the labels in the images, unlike segment-level methods. Also, they cannot take advantage of training images that are only locally similar to a test image. We propose several ways to combine recent image-level and segment-level techniques to predict both image and segment labels jointly. We cast our experimental study in an unified framework for both image-level and segment-level annotation tasks. On three challenging datasets, our joint prediction of image and segment labels outperforms either prediction alone on both tasks. This confirms that the two levels offer complementary information. More... »

PAGES

16-28

References to SciGraph publications

  • 2004-09. Efficient Graph-Based Image Segmentation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008. A New Baseline for Image Annotation in COMPUTER VISION – ECCV 2008
  • 2010. Energy Minimization under Constraints on Label Counts in COMPUTER VISION – ECCV 2010
  • 2006. Coloring Local Feature Extraction in COMPUTER VISION – ECCV 2006
  • 2006. TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation in COMPUTER VISION – ECCV 2006
  • 2008-05. Evaluation of Localized Semantics: Data, Methodology, and Experiments in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2002. Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary in COMPUTER VISION — ECCV 2002
  • Book

    TITLE

    Advances in Multimedia Modeling

    ISBN

    978-3-642-27354-4
    978-3-642-27355-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-27355-1_5

    DOI

    http://dx.doi.org/10.1007/978-3-642-27355-1_5

    DIMENSIONS

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