The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
On Improving Clustering in Numerical Databases with Artificial Ants
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the performance of ant-based clustering
Design and application of hybrid intelligent systems
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Aggregation Pheromone Density Based Clustering
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Hybridization of the ant colony optimization with the k-means algorithm for clustering
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Use of aggregation pheromone density for image segmentation
Pattern Recognition Letters
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
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Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This paper presents a novel method for image segmentation considering the aggregation behavior of ants. Image segmentation is viewed as a clustering problem which aims to partition a given set of pixels into a number of homogenous clusters/segments. At each location of data point representing a pixel an ant is placed; and the ants are allowed to move in the search space to find out the points with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different points of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of images using different cluster validity measures. Results are compared with those obtained using average linkage and k-means clustering algorithms and are found to be better.