Image Hierarchical Segmentation Based on a GHSOM

Abstract

A novel approach for image segmentation is proposed in this paper. This approach is based on the growing hierarchical self-organizing map (GHSOM), which consists of a hierarchical architecture composed of growing self-organizing maps (SOMs). The SOMs have shown to be successful for the analysis of high-dimensional input data as in data mining applications. The hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, mirroring inherent hierarchical relations among input data. Image hierarchical segmentation can be achieved by using this neural network model, where the hierarchical structure of segmented regions is captured. In order to evaluate the performance of this segmentation method, an application for hierarchical background modeling in video sequences is provided. Therefore, foreground detection is achieved. Experimental results show that the proposed approach is promising for applications where hierarchical segmentation is required.