International Conference on Multimedia Modeling

MMM 2013: Advances in Multimedia Modeling pp 1-12

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

  • Symeon Papadopoulos
  • Christos Sagonas
  • Ioannis Kompatsiaris
  • Athena Vakali
Conference paper

DOI: 10.1007/978-3-642-35725-1_1

Volume 7732 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Papadopoulos S., Sagonas C., Kompatsiaris I., Vakali A. (2013) Semi-supervised Concept Detection by Learning the Structure of Similarity Graphs. In: Li S. et al. (eds) Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg

Abstract

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.

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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