Multiresolution Segmentation: a Parallel Approach for High Resolution Image Segmentation in Multicore Architectures

In automatic image interpretation, the process of extracting different objects that compose an image is one of the primary steps. This process is known as image segmentation and consists of subdividing an image into meaningful regions, also called segments, which will be classified in a later step. Many of the existing segmentation algorithms, however, have high computational cost for large images as the currently high-resolution remote sensing images. The main focus of this paper is to tackle this problem by using parallel processing. The idea is to explore current multi-core architectures available in commercial processors in order to speedup the segmentation process. A multithreading parallel implementation of a region growing algorithm proposed originally by Baatz and Schäpe (2000) is presented that aims at providing better execution times, while delivering a similar outcome produced by the sequential version. The algorithm is able to work with any number of threads, which is defined as an input parameter, so as to take full advantage of the upcoming processors having any number of cores. The current parallel implementation was tested on three different images on a quad-core processor and obtained up to 2.6 of segmentation speedup.