Supervised and unsupervised clustering with probabilistic shift

Sanketh Shetty, Narendra Ahuja

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


We present a novel scale adaptive, nonparametric approach to clustering point patterns. Clusters are detected by moving all points to their cluster cores using shift vectors. First, we propose a novel scale selection criterion based on local density isotropy which determines the neighborhoods over which the shift vectors are computed. We then construct a directed graph induced by these shift vectors. Clustering is obtained by simulating random walks on this digraph. We also examine the spectral properties of a similarity matrix obtained from the directed graph to obtain a K-way partitioning of the data. Additionally, we use the eigenvector alignment algorithm of [1] to automatically determine the number of clusters in the dataset. We also compare our approach with supervised[2] and completely unsupervised spectral clustering[1], normalized cuts[3], K-Means, and adaptive bandwidth meanshift[4] on MNIST digits, USPS digits and UCI machine learning data.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
Number of pages14
EditionPART 5
ISBN (Print)3642155545, 9783642155543
StatePublished - 2010
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: Sep 10 2010Sep 11 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume6315 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th European Conference on Computer Vision, ECCV 2010
CityHeraklion, Crete


  • Data Clustering
  • Image Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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