Semantic image segmentation has recently become the focus of considerable interest. This task consists in assigning a predefined class label to each pixel (or pre-segmented region) in an image. To address the complexity challenge of this task, we develop, in this work, a novel and simple energy-minimization model. The proposed cost function of this model combines efficiently different global non-parametric semantic likelihood energy terms computed from the (pre-)segmented regions of the (query) image and their structural properties (location, texture, color, context and shape). To optimize our energy-based model, we use a local optimization procedure derived from the iterative conditional modes (ICM) algorithm. Experimental results on the challenging Microsoft Research Cambridge dataset (MSRC-21) clearly shows the feasibility and the merits of the proposed approach.