We present a computational approach to main subject detection, which provides a measure of saliency or importance for different regions that are associated with different subjects in an image with unconstrained scene content. It is built primarily upon selected image semantics, with low-level vision feature also contributing to the decision. The algorithm consists of region segmentation, perceptual grouping, feature extraction, and probabilistic reasoning. To accommodate the inherent ambiguity in the problem as reflected by the ground truth, we have developed a novel training mechanism for Bayes nets- based on fractional frequency counting. Using a set of images spanning the 'photo space', experimental results have shown the promise of our approach in that most of the regions that independent observers ranked as the main subject are also labeled as such by our system. In addition, our approach lends itself to performance scalable configurations within the Bayes net-based framework. Different applications have different degrees of tolerance to performance degradation and sped aggravation; computing a full set of features may be not practical for time- critical applications. We have designed the algorithm to run under three configurations, without reorganization or retraining of the network.