Semi-supervised Concept Detection by Learning the Structure of Similarity Graphs

  • Symeon Papadopoulos
  • Christos Sagonas
  • Ioannis Kompatsiaris
  • Athena Vakali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7732)


We present an approach for detecting concepts in images by a graph-based semi-supervised learning scheme. The proposed approach builds a similarity graph between both the labeled and unlabeled images of the collection and uses the Laplacian Eigemaps of the graph as features for training concept detectors. Therefore, it offers multiple options for fusing different image features. In addition, we present an incremental learning scheme that, given a set of new unlabeled images, efficiently performs the computation of the Laplacian Eigenmaps. We evaluate the performance of our approach both on synthetic datasets and on MIR Flickr, comparing it with high-performance state-of-the-art learning schemes with competitive and in some cases superior results.


Similarity Graph Result Fusion Multiple Kernel Learn Concept Detection Unlabeled Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Symeon Papadopoulos
    • 1
  • Christos Sagonas
    • 1
  • Ioannis Kompatsiaris
    • 1
  • Athena Vakali
    • 2
  1. 1.Information Technologies InstituteCERTHGreece
  2. 2.Informatics DepartmentAristotle University of ThessalonikiGreece

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