From frequent itemsets to semantically meaningful visual patterns

Junsong Yuan*, Ying Wu, Ming Yang

*Corresponding author for this work

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

42 Scopus citations

Abstract

Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures. It is not a trivial task to discover meaningful visual patterns in image databases, because the content variations and spatial dependency in the visual data greatly challenge most existing methods. This paper presents a novel approach to coping with these difficulties for mining meaningful visual patterns. Specifically, the novelty of this work lies in the following new contributions: (1) a principled solution to the discovery of meaningful itemsets based on frequent itemset mining; (2) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning; and (3) a pattern summarization method that deals with the measurement noises brought by the image data. The experimental results in the real images show that our method can discover semantically meaningful patterns efficiently and effectively.

Original languageEnglish (US)
Title of host publicationKDD-2007
Subtitle of host publicationProceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages864-873
Number of pages10
DOIs
StatePublished - Dec 14 2007
EventKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Jose, CA, United States
Duration: Aug 12 2007Aug 15 2007

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySan Jose, CA
Period8/12/078/15/07

Keywords

  • Image data mining
  • Meaningful itemset mining
  • Pattern summarization
  • Self-supervised clustering

ASJC Scopus subject areas

  • Software
  • Information Systems

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