Publication:
Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals
Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals
| datacite.subject.fos | oecd::Engineering and technology | |
| dc.contributor.author | Dinh Phamtoan | |
| dc.contributor.author | Tai Vovan | |
| dc.date.accessioned | 2022-11-02T02:03:11Z | |
| dc.date.available | 2022-11-02T02:03:11Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This research proposes the method to extract the characteristics of images to become the intervals. These intervals are used to build the automatic fuzzy genetic algorithm for images (AFGI). In the proposed model, the overlap measure is the criterion to evaluate the closeness of intervals, and the new Davies and Bouldin index is the objective function. The AFGI can determine the proper number of clusters, the images in each cluster, and the probability to belong to clusters of images at the same time. The experiments with different types of images illustrate the steps of AFGI, and show its significant benefit in comparing to other algorithms. | |
| dc.identifier.doi | 10.1007/s11042-020-09975-3 | |
| dc.identifier.uri | http://repository.vlu.edu.vn:443/handle/123456789/601 | |
| dc.language.iso | en_US | |
| dc.relation.ispartof | Multimedia Tools and Applications | |
| dc.relation.issn | 1380-7501 | |
| dc.relation.issn | 1573-7721 | |
| dc.subject | "Cluster analysis | |
| dc.subject | Fuzzy genetic algorithm | |
| dc.subject | Image processing | |
| dc.subject | Interval data | |
| dc.subject | Pattern recognition | |
| dc.subject | Unsupervised learning" | |
| dc.title | Automatic fuzzy genetic algorithm in clustering for images based on the extracted intervals | |
| dc.type | journal-article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 28-29 | |
| oaire.citation.volume | 80 |