Multi-scale saliency detection via inter-regional shortest colour path
Saliency detection has attracted considerable attention, and numerous approaches aimed at locating meaningful regions in images have been presented. Nevertheless, accurate saliency detection algorithms remain in urgent demand. Many algorithms work well when dealing with simple images, but work poorly with complex images that contain small-scale and high-contrast structures. Moreover, most existing local and global regional saliency detection methods measure image saliency through region contrast. Such measurement is achieved by directly computing the difference between non-adjacent regions. In this study, the authors introduce a new perspective for evaluating region contrast. We propose a novel multi-scale saliency region detection method by optimising the shortest path of two non-adjacent regions in the colour space and by measuring the region contrast from different scales. The final saliency maps indicate that the proposed method can work well with images containing small patches, but with high contrast. The proposed approach can also make the foreground significantly more uniform. Experimental results on three public benchmark datasets show that the proposed method achieves better precision–recall curve than some state-of-the-art methods.
