Extraction of flower regions in color images using ant colony optimization

Extraction of flower regions from complex background is a difficult task and it is an important part of a flower image retrieval and recognition. In this article, we propose an Ant Colony Optimization (ACO) algorithm as a general color clustering method, and test it on flower images as a case study of object boundary extraction. The segmentation methodology on flower images consists of six steps: color space conversion, generation of candidate color cluster centers, ant colony optimization method to select optimum color cluster centers, merging of cluster centers which are close to each other, image segmentation by clustering, and extraction of flower region from the image. To evince that ACO algorithm can be a general segmentation method, some results of natural images in Berkeley segmentation benchmark have been presented. The method as a case study on flower region extraction has also been tested on the images of Oxford-17 Flowers dataset, and the results have confronted with other well established flower region extraction approaches.