A Multi-Objective Graph-based Genetic Algorithm for image segmentation

Image Segmentation is one of the most challenging problems in Computer Vision. This process consists in dividing an image in different parts which share a common property, for example, identify a concrete object within a photo. Different approaches have been developed over the last years. This work is focused on Unsupervised Data Mining methodologies, specially on Graph Clustering methods, and their application to previous problems. These techniques blindly divide the image into different parts according to a criterion. This work applies a Multi-Objective Genetic Algorithm in order to perform good clustering results comparing to classical and modern clustering algorithms. The algorithm is analysed and compared against different clustering methods, using a precision and recall evaluation, and the Berkeley Image Database to carry out the experimental evaluation.