- Title
- Image clustering using a similarity measure incorporating human perception
- Creator
- Shojanazeri, Hamid; Aryal, Sunil; Teng, Shyh; Zhang, Dengsheng; Lu, Guojun
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/169294
- Identifier
- vital:13995
- Identifier
-
https://doi.org/10.1109/IVCNZ.2018.8634744
- Identifier
- ISBN:2151-2191 (ISSN) 978-1-7281-0125-5 (ISBN)
- Abstract
- 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 l(p) - norm and mass-based m(p) - dissimilarity.
- Publisher
- IEEE
- Relation
- 2018 International Conference on Image and Vision Computing New Zealand, IVCNZ 2018; Auckland, New Zealand; 19th-21st November 2018 p. 1-6
- Rights
- Copyright © 2018 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Image clustering; K-Medoids; Euclidean distance; mp - dissimilarity; Perceptual dissimilarity measure
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