Integrating unsupervised and supervised clustering methods on a GPU platform for fast image segmentation

Aim of the paper is to demonstrate how by integrating unsupervised and supervised parallel neural clustering methods in a GPU platform we may carry out a fast image segmentation with a satisfactory compromise between the topological preservation of the original image and the minimization of the quantization error, also known as clustering accuracy. For this reason, an unsupervised parallel clustering method inspired by the Extended SOM (ESOM) powered by a Learning Vector Quantization (LVQ) like algorithm is proposed. Then, its parallel supervised versions is presented to further minimize the quantization error in case proper prototypes of the desired clusters are known. Finally, the GPU implementation of both these methods are illustrated to show how we may support time critical tasks such as real time surveillance, interactive medical diagnosis, and control of dynamical systems. The performance of the GPU implementation is discussed with the help of small examples and realistic processing tasks.