On Graph Extraction from Image Data

Andreas Holzinger, Bernd Malle, Nicola Giuliani

Research output: Chapter in Book/Report/Conference proceedingChapter


Hot topics in knowledge discovery and interactive data mining from natural images include the application of topological methods and machine learning algorithms. For any such approach one needs at first a relevant and robust digital content representation from the image data. However, traditional pixel-based image analysis techniques do not effectively extract, hence represent the content. A very promising approach is to extract graphs from images, which is not an easy task. In this paper we present a novel approach for knowledge discovery by extracting graph structures from natural image data. For this purpose, we created a framework built upon modern Web technologies, utilizing HTML canvas and pure Javascript inside a Web-browser, which is a very promising engineering approach. Following on a short description of some popular image classification and segmentation methodologies, we outline a specific data processing pipeline suitable for carrying out future scientific research. A demonstration of our implementation, compared to the results of a traditional watershed transformation performed in Matlab showed very promising results in both quality and runtime, despite
some open problems. Finally, we provide a short discussion of a few open problems and outline some of our future research routes
Original languageEnglish
Title of host publicationBrain Informatics and Health, BIH 2014, Lecture Notes in Artificial Intelligence LNAI 8609
Place of PublicationHeidelberg, Berlin
Publication statusPublished - 2014


  • Knowledge Discovery
  • data mining
  • graphs

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)
  • Theoretical
  • Experimental


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