@article {10.3844/jcssp.2007.162.167,
article_type = {journal},
title = {An Efficient Ant Algorithm for Swarm-Based Image Clustering},
author = {Ouadfel, Salima and Batouche, Mohamed},
volume = {3},
number = {3},
year = {2007},
month = {Mar},
pages = {162-167},
doi = {10.3844/jcssp.2007.162.167},
url = {https://thescipub.com/abstract/jcssp.2007.162.167},
abstract = {A collective approach to resolve the segmentation problem was proposed. AntClust is a new ant-based algorithm that uses the self-organizing and autonomous brood sorting behavior observed in real ants. Ants and pixels are scatted on a discrete array of cells represented the ants’ environment. Using simple local rules and without any central control, ants form homogeneous clusters by moving pixels from the cells of the array according to a local similarity function. The initial knowledge of the number of clusters and initial partition were not needed during the clustering process. Experimental results conducted on synthetic and real images demonstrate that our algorithm AntClust was able to extract the correct number of clusters with good clustering quality compared to the results obtained from a classical clustering algorithm like Kmeans algorithm.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}