The segmentation of colour images (RGB), distinguishing clusters of image points, representing for example background, leaves and flowers, is performed in a multi-dimensional environment. Considering a two dimensional environment, clusters can be divided by lines. In a three dimensional environment by planes and in an n-dimensional environment by n-1 dimensional structures. Starting with a complete data set the first neural network, represents an n-1 dimensional structure to divide the data set into two subsets. Each subset is once more divided by an additional neural network: recursive partitioning. This results in a tree structure with a neural network in each branching point. Partitioning stops as soon as a partitioning criterium cannot be fulfilled. After the unsupervised training the neural system can be used for the segmentation of images.
|Title of host publication||Proceedings of the Third International Symposium on Sensors in Horticulture, ISHS - Sensors in Horticulture, Tiberias (IL), August 1997|
|Publication status||Published - 2001|
Gieling, T. H., Janssen, H. J. J., de Vries, H. C., & Loef, P. (2001). Unsupervised image segmentation with neural networks. In Proceedings of the Third International Symposium on Sensors in Horticulture, ISHS - Sensors in Horticulture, Tiberias (IL), August 1997 (pp. 100-108). (Acta Horticulturae; No. 562). https://edepot.wur.nl/24308