A Bayesian network-based approach for identifying regions of interest utilizing global image features

An image-understanding algorithm for identifying Regions-of-Interest (ROI) in digital images is proposed. Global and regional features that characterize relations between image segments are fused in a probabilistic framework to generate ROI for an arbitrary image. Features are introduced as maps for spatial position, weighted similarity, and weighted homogeneity for image regions. The proposed methodology 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 show a competitive performance in comparison with the state-of- the-art techniques with an accuracy rate of ~80% on a set of ~20,000 publicly available color images. Applications of the proposed algorithm include content-based image retrieval, image indexing, automatic image annotation, mobile phone imagery, and digital photo cropping.