Color Segmentation Using Self-Organizing Feature Maps (SOFMs) Defined Upon Color and Spatial Image Space

A novel approach to color image segmentation is proposed and formulated in this paper. Conventional color segmentation methods apply SOFMs – among other techniques – as a first stage clustering in hierarchical or hybrid schemes in order to achieve color reduction and enhance robustness against noise. 2-D SOFMs defined upon 3-D color space are usually employed to render the distribution of colors of an image without taking into consideration the spatial correlation of color vectors throughout various regions of the image. Clustering color vectors pertaining to segments of an image is carried out in a consequent stage via unsupervised or supervised learning. A SOFM defined upon the 2-D image plane, which is viewed as a spatial input space, as well as the output 3-D color space is proposed. Two different initialization schemes are performed, i.e. uniform distribution of the weights in 2-D input space in an ordered fashion so that information regarding local correlation of the color vectors is preserved and jointly uniform distribution of the weights in both 3-D color space and 2-D input space. A second stage of Density-Based Clustering of the nodes of the SOM (utilizing an ad hoc modification of the DBSCAN algorithm) is employed in order to facilitate the segmentation of the color image.