Mining images of material nanostructure data

Aparna Varde; Jianyu Liang; Elke Rundensteiner; Richard Sisson

doi:10.1007/11951957_35

Mining images of material nanostructure data

Aparna Varde, Jianyu Liang, Elke Rundensteiner, Richard Sisson

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

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 language	English
Title of host publication	Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006
Editors	Sanjay K. Madria, Kajal T. Claypool, Rajgopal Kannan, Prem Uppuluri, Manoj Madhava Gore
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	403-413
Number of pages	11
ISBN (Print)	9783540683797
DOIs	https://doi.org/10.1007/11951957_35
State	Published - 2006
Event	3rd International Conference on Distributed Computing and Internet Technology, ICDCIT 2006 - Bhubaneswar, India Duration: 20 Dec 2006 → 23 Dec 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4317
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Distributed Computing and Internet Technology, ICDCIT 2006
Country/Territory	India
City	Bhubaneswar
Period	20/12/06 → 23/12/06

Keywords

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

Access to Document

10.1007/11951957_35

Cite this

Varde, A., Liang, J., Rundensteiner, E., & Sisson, R. (2006). Mining images of material nanostructure data. In S. K. Madria, K. T. Claypool, R. Kannan, P. Uppuluri, & M. M. Gore (Eds.), Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006 (pp. 403-413). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4317). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/11951957_35

Varde, Aparna ; Liang, Jianyu ; Rundensteiner, Elke et al. / Mining images of material nanostructure data. Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006. editor / Sanjay K. Madria ; Kajal T. Claypool ; Rajgopal Kannan ; Prem Uppuluri ; Manoj Madhava Gore. Springer Science and Business Media Deutschland GmbH, 2006. pp. 403-413 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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author = "Aparna Varde and Jianyu Liang and Elke Rundensteiner and Richard Sisson",

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doi = "10.1007/11951957_35",

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Varde, A, Liang, J, Rundensteiner, E & Sisson, R 2006, Mining images of material nanostructure data. in SK Madria, KT Claypool, R Kannan, P Uppuluri & MM Gore (eds), Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4317, Springer Science and Business Media Deutschland GmbH, pp. 403-413, 3rd International Conference on Distributed Computing and Internet Technology, ICDCIT 2006, Bhubaneswar, India, 20/12/06. https://doi.org/10.1007/11951957_35

Mining images of material nanostructure data. / Varde, Aparna; Liang, Jianyu; Rundensteiner, Elke et al.
Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006. ed. / Sanjay K. Madria; Kajal T. Claypool; Rajgopal Kannan; Prem Uppuluri; Manoj Madhava Gore. Springer Science and Business Media Deutschland GmbH, 2006. p. 403-413 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4317).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Mining images of material nanostructure data

AU - Varde, Aparna

AU - Liang, Jianyu

AU - Rundensteiner, Elke

AU - Sisson, Richard

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2006.

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

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KW - Image Mining

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KW - Notion of Similarity

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006

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A2 - Claypool, Kajal T.

A2 - Kannan, Rajgopal

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Y2 - 20 December 2006 through 23 December 2006

ER -

Varde A, Liang J, Rundensteiner E, Sisson R. Mining images of material nanostructure data. In Madria SK, Claypool KT, Kannan R, Uppuluri P, Gore MM, editors, Distributed Computing and Internet Technology - 3rd International Conference, ICDCIT 2006. Springer Science and Business Media Deutschland GmbH. 2006. p. 403-413. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11951957_35

Mining images of material nanostructure data

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