Cluster images with AntClust: a clustering algorithm based on the chemical recognition system of ants
This work addresses clustering challenges in computer vision, particularly for vehicle re-identification, but is incremental as it adapts an existing bio-inspired method to a new domain.
The paper tackles image clustering by implementing AntClust, a bio-inspired algorithm based on ant chemical recognition, and tests it on a car image dataset, achieving performance comparable to established methods like DBSCAN, HDBSCAN, and OPTICS.
We implement AntClust, a clustering algorithm based on the chemical recognition system of ants and use it to cluster images of cars. We will give a short recap summary of the main working principles of the algorithm as devised by the original paper [1]. Further, we will describe how to define a similarity function for images and how the implementation is used to cluster images of cars from the vehicle re-identification data set. We then test the clustering performance of AntClust against DBSCAN, HDBSCAN and OPTICS. Finally one of the core parts in AntClust, the rule set can be easily redefined with our implementation, enabling a way for other bio-inspired algorithms to find rules in an automated process. The implementation can be found on GitLab [9].