Fuzzy and non-fuzzy approaches for digital image classification

Diykh, Mohammed and Li, Yan (2016) Fuzzy and non-fuzzy approaches for digital image classification. Journal of Theoretical and Applied Information Technology , 95 (4). pp. 858-870. ISSN 1992-8645

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This paper classifies different digital images using two types of clustering algorithms. The first type is the fuzzy clustering methods, while the second type considers the non-fuzzy methods. For the performance comparisons, we apply four clustering algorithms with two from the fuzzy type and the other two from the non-fuzzy (partitonal) clustering type. The automatic partitional clustering algorithm and the partitional k-means algorithm are chosen as the two examples of the non-fuzzy clustering techniques, while the automatic fuzzy algorithm and the fuzzy C-means clustering algorithm are taken as the examples of the fuzzy clustering techniques. The evaluation among the four algorithms are done by implementing these algorithms to three different types of image databases, based on the comparison criteria of: dataset size, cluster number, execution time and classification accuracy and k-cross validation. The experimental results demonstrate that the non-fuzzy algorithms have higher accuracies in compared to the fuzzy algorithms, especially when dealing with large data sizes and different types of images. Three types of image databases of human face images, handwritten digits and natural scenes are used for the performance evaluation.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: No
Item Status: Live Archive
Additional Information: Published version made available according to Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0 International License.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 11 Apr 2017 01:11
Last Modified: 04 Jul 2017 03:15
Uncontrolled Keywords: clustering algorithms; fuzzy clustering; C-Means clustering; K-means clustering; partitional clustering
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: non available
URI: http://eprints.usq.edu.au/id/eprint/31224

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