Integral split-and-merge methodology for real-time image segmentation

The segmentation of images is a critical step in many computer vision applications. Additionally, some applications require the achievement of acceptable segmentation quality while the algorithm is executed in real time. In this study, we present a split-and-merge segmentation methodology that uses integral images to improve the execution time. We call our methodology integral split and merge (ISM) segmentation. The integral images are used here to calculate statistics of the image regions in constant time. Those statistics are used to guide the splitting process by identifying the homogeneous regions in the image. We also propose a merge criterion that performs connected component analysis of the homogeneous regions. Moreover, the merging procedure is able to group regions of the image showing gradients. Furthermore, the number of regions resulting from the segmentation process is determined automatically. In a series of tests, we compare ISM against other state-of-the-art algorithms. The results from the tests show that our ISM methodology obtains image segmentations with a comparable quality, using a simple texture descriptor instead of a combination of color-texture descriptors. The proposed ISM methodology also has a piecewise linear computational complexity, resulting in an algorithm fast enough to be executed in real time. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.