Mining images of material nanostructure data

Aparna Varde, Jianyu Liang, Elke Rundensteiner, Richard Sisson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Scientific datasets often consist of complex data types such as images. Mining such data presents interesting issues related to semantics. In this paper, we explore the research issues in mining data from the field of nanotechnology. More specifically, we focus on a problem that relates to image comparison of material nanostructures. A significant challenge here relates to the notion of similarity between the images. Features such as size and height of nano-particles and inter-particle distance are important in image similarity as conveyed by domain experts. However, there are no precise notions of similarity defined apriori. Hence there is a need for learning similarity measures. In this paper, we describe our proposed approach to learn similarity measures for graphical data. We discuss this with reference to nanostructure images. Other challenges in image comparison are also outlined. The use of this research is discussed with respect to targeted applications.

Original languageEnglish
Title of host publicationDistributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006
EditorsSanjay K. Madria, Kajal T. Claypool, Rajgopal Kannan, Prem Uppuluri, Manoj Madhava Gore
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783540683797
StatePublished - 2006
Event3rd International Conference on Distributed Computing and Internet Technology, ICDCIT 2006 - Bhubaneswar, India
Duration: 20 Dec 200623 Dec 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Distributed Computing and Internet Technology, ICDCIT 2006


  • Data Visualization
  • Image Mining
  • Interestingness Measures
  • Nanotechnology
  • Notion of Similarity
  • Scientific Databases


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