Automatic Image Classification Using the Classification Ant-Colony Algorithm
Wei-jiu Zhang, Li Mao, Wenbo Xu · 2009
To enhance the versatility, robustness, and convergence rate of automatic classification of images, an ant-colony-based classification model is proposed in this paper. According to the characteristics of the image classification, this model adopts and improves the traditional Ant-Colony algorithm. It defines two types of ants that have different search strategies and refreshing mechanisms. The stochastic ants identify new categories, construct the category tables and determine the clustering center of each category. The Intellectual ants classify the image pixels using their search advancing strategies, with the guidance of the information provided by stochastic ants. Comparing with the traditional ant colony algorithms, this algorithm provides a more effective and accurate approach for automatic image classification.