We propose an image-understanding algorithm for identifying and ranking regions of perceptually relevant content in digital images. Global features that characterize relations between image regions are fused in a probabilistic framework to generate a region ranking map (RRM) of an arbitrary image. Features are introduced as maps for spatial position, weighted similarity, and weighted homogeneity for image regions. Further analysis of the RRM, based on the receiver operating characteristic curve, has been utilized to generate a binary map that signifies region of interest in the test image. The algorithm includes modules for image segmentation, feature extraction, and probabilistic reasoning. It differs from prior art by using machine learning techniques to discover the optimum Bayesian Network structure and probabilistic inference. It also eliminates the necessity for semantic understanding at intermediate stages. Experimental results indicate an accuracy rate of ~90% on a set of ~4000 color images that are publicly available and compare favorably to state-of-the-art techniques. Applications of the proposed algorithm include smart image and document rendering, content-based image retrieval, adaptive image compression and coding, and automatic image annotation.