Salient Region Detection Using Self-Guided Statistical Non-Redundancy in Natural Images

Abstract—We propose an effective framework for salient re-gion detection in natural images based on the concept of self-guided statistical non-redundancy (SGNR). Salient regions are unique because they have low information redundancy within a given image, while the rest of the scene may be highly redundant. We first analyze the structural characteristics of the image using structured image elements (samples) and classify them as being non-redundant or redundant based on textural compactness and overall non-redundancy. This guides saliency detection towards regions with low information redundancy by considering explicitly high information redundancy of samples potentially belonging to the background. We then compute the saliency map by determining the statistical non-redundancy of each sample using a conditional graph model. Experimental results based on publicly available datasets show that SGNR provides promising results when compared to existing saliency approaches. Index Terms—Non-redundancy, salient region detection, image segmentation I