Model-based classification and novelty detection for point pattern data

Ba-Ngu Vo, Nhat-Quang Tran, Dinh Phung, Ba-Tuong Vo

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

6 Citations (Scopus)


Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood functions, and a maximum likelihood estimator for learning a tractable family of RFS models. In novelty detection, we propose novel ranking functions based on RFS models, which substantially improve performance.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR 2016)
Subtitle of host publicationCancún Center, Cancún, México, December 4-8, 2016
EditorsLarry Davis, Alberto Del Bimbo, Brian C. Lovell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509048472
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference on Pattern Recognition 2016 - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23rd (Proceedings)


ConferenceInternational Conference on Pattern Recognition 2016
Abbreviated titleICPR 2016
Internet address


  • Classification
  • Multiple instance data
  • Naive Bayes model
  • Novelty detection
  • Point pattern data
  • Point process
  • Random finite set

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