Element-Level Clustering of Feature Vectors Considering Correlations for Analyzing Image Data

Masako Omachi, Shinichiro Omachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements, the correlation is used as the metric for clustering. In order to effectively solve the optimization problem, a technique for graph cut is used. After dividing the set of elements into subsets, clustering is performed for each subset. Experiments using a handwritten image database show the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2016 12th International Conference on Semantics, Knowledge and Grids, SKG 2016
EditorsHai Zhuge, Xiaoping Sun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-149
Number of pages4
ISBN (Electronic)9781509047956
DOIs
Publication statusPublished - 2017 Jan 11
Event12th International Conference on Semantics, Knowledge and Grids, SKG 2016 - Beijing, China
Duration: 2016 Aug 152016 Aug 17

Publication series

NameProceedings - 2016 12th International Conference on Semantics, Knowledge and Grids, SKG 2016

Other

Other12th International Conference on Semantics, Knowledge and Grids, SKG 2016
CountryChina
CityBeijing
Period16/8/1516/8/17

Keywords

  • clustering
  • correlation
  • image analysis
  • stochastic model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications

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