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We propose an effective framework for salient region 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 highly be 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 toward 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 data sets show that SGNR provides promising results when compared with existing saliency approaches.

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