Name
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
<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex>
-Medoids clustering show that uses the hybrid Perceptual Dissimilarity Measure (PMD)produces better clustering results than distance-based
<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{p}$</tex>
- norm and mass-based
<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{p}$</tex>
- dissimilarity. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IVCNZ.2018.8634744 | IVCNZ |
Keywords | Field | DocType |
Clustering algorithms,Task analysis,Standards,Image color analysis,Euclidean distance,Australia,Image retrieval | Pairwise comparison,Computer vision,Pattern recognition,Task analysis,Similarity measure,Computer science,Euclidean distance,Image retrieval,Image processing,Artificial intelligence,Cluster analysis,Medoid | Conference |
ISSN | ISBN | Citations |
2151-2191 | 978-1-7281-0125-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamid Shojanazeri | 1 | 0 | 0.68 |
| Sunil Aryal | 2 | 38 | 8.23 |
| Shyh Wei Teng | 3 | 151 | 21.02 |
| Dengsheng Zhang | 4 | 2462 | 100.00 |
| Guojun Lu | 5 | 124 | 9.01 |