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Image clustering using a similarity measure incorporating human perception

Version 2 2024-06-05, 02:47
Version 1 2019-04-28, 10:38
conference contribution
posted on 2024-06-05, 02:47 authored by H Shojanazeri, Sunil AryalSunil Aryal, SW Teng, D Zhang, G Lu
Clustering similar images is an important task in image processing and computer vision. It requires a measure to quantify pairwise similarities of images. The performance of clustering algorithm depends on the choice of similarity measure. In this paper, we investigate the effectiveness of data-independent (distance-based), data-dependent (mass-based)and hybrid (dis)similarity measures in the image clustering task using three benchmark image collections with different sets of features. Our results of K-Medoids clustering show that uses the hybrid Perceptual Dissimilarity Measure (PMD)produces better clustering results than distance-based ℓp-norm and mass-based mp-dissimilarity.

History

Pagination

1-6

Location

Auckland, New Zealand

Start date

2018-11-19

End date

2018-11-21

ISSN

2151-2205

ISBN-13

9781728101255

Language

eng

Notes

keywords: computer vision;pattern clustering;hybrid perceptual dissimilarity measure;image clustering algorithm;K-medoids clustering;PMD;distance-based ℓp-norm;mass-based mp-dissimilarity;benchmark image collections;data-dependent;data-independent;computer vision;image processing;human perception;Clustering algorithms;Task analysis;Standards;Image color analysis;Euclidean distance;Australia;Image retrieval;Image clustering;K-Medoids;Euclidean distance;$m_p$ - dissimilarity;perceptual dissimilarity measure

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2018, IEEE

Title of proceedings

IVCNZ 2018 : International Conference on Image and Vision Computing New Zealand

Event

Image and Vision Computing New Zealand. International Conference (2018 : Auckland, New Zealand)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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