A computational approach to determination of main subject regions in photographic images
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 features 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 (probabilistic in nature), 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, without reorganization and retraining, the Bayes net-based framework lends itself to performance scalable configurations to suit different applications that have different requirements of accuracy and speed. This paper focuses on a high level description of the complete system used to solve the overall problem, while providing necessary descriptions of the component algorithms.
main subject saliency scalable configuration image understanding region of interest semantic object class belief networks observer studies