Analysing the Structure of Semantic Concepts in Visual Databases

  • Mats Sjöberg
  • Jorma Laaksonen
Conference paper

DOI: 10.1007/978-3-642-21566-7_34

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6731)
Cite this paper as:
Sjöberg M., Laaksonen J. (2011) Analysing the Structure of Semantic Concepts in Visual Databases. In: Laaksonen J., Honkela T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg

Abstract

In this paper we study how the Self-Organizing Map (SOM) can be used in analysing the structure of semantic concepts in visual data. We investigate two data sets with concept labels provided by humans, and unlabelled data for which we utilise automatically detected concept membership scores by using models trained on a labelled data set. By arranging the concept memberships of visual objects as components of a vector, they can be used as the feature space for training a SOM. A visual and qualitative analysis of the SOM distributions of different concepts is augmented with a quantitative analysis based on measuring the Earth Mover’s Distance between the vector distributions on the 2D SOM surface. In particular we study the PASCAL VOC 2007 and TRECVID 2010 databases, which are two large image and video data sets.

Keywords

Self-Organizing Map Earth Mover’s Distance concept detection high-level features image and video databases visualisation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mats Sjöberg
    • 1
  • Jorma Laaksonen
    • 1
  1. 1.Adaptive Informatics Research CentreAalto University School of ScienceAaltoFinland

Personalised recommendations