Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 535–544 (2018)
Abstract. To implement effective computer vision mechanisms, effective image clustering methods are important. The paper elaborates a clustering method based on particle swarm optimization (PSO) which provides automatic establishment of clustering parameters. The developed PSO based clustering method was tested on 860 images for a car vision system and its results and contribution to the pattern recognition quality improvement were assessed in comparison with fuzzy C-means and k-means. The results do not differ significantly, but distinction in average time of work of these methods was noted. PSO clustering method is faster than k-means and slower than fuzzy C-means. However, fuzzy C-means method does not guarantee correct results during the further analysis, so PSO clustering method can be more efficient for implementation in computer vision systems.
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