Color saliency model based on mean shift segmentation

Saliency detection is one of the extraordinary capabilities of the human visual system (HVS). In this paper, we present a novel saliency detection model to capture visual selective attention of images. The new model does not require prior knowledge of salient regions as well as manual labeling. The mean shift segmentation algorithm and quaternion discrete cosine transform (QDCT) are used to generate a rough saliency map by integrating low-level features and spatial saliency information. In each segmented region, the color saliency is measured based on the probability of its occurrences in foreground and background defined by the rough saliency map. The experimental results on a widely used benchmark database demonstrated that the presented model achieves the best performance in terms of visual and quantitative evaluations compared to existing state-of-the-art saliency detection models.