Towards semantic segmentation of orthophoto images using graph-based community identification

We present an unsupervised framework that automatically detects objects of interest in images by formulating the general problem of semantic segmentation as community detection problem in graphs. The framework broadly follows a four-step procedure. First, we perform an over-segmentation of the original image using the well-known statistical region merging algorithm which presents the advantage of not requiring any quantization or colour space transformations. Second, we compute the feature descriptors of the resulting segmented regions. For encoding colour and other textural information, each region is described by an hybrid descriptor based on colour histograms and covariance matrix descriptor. Third, from the set of descriptors we construct different weighted graphs using various graph construction algorithms. Finally, the resulting graphs are then divided into groups or communities using a community detection algorithm based on spectral modularity maximization. This algorithm makes use of the eigenspectrum of matrices such as the graph Laplacian matrix and the modularity matrix which are more likely to reveal the community structure of the graph. Experiments conducted on large orthophotos depicting several zones in the region of Belfort city situated on the north-eastern of France provide promising results. The proposed framework can be used by semi-automatic approaches to handle the challenging problems of scene parsing.