CF-based optimisation for saliency detection

In view of the observation that saliency maps generated by saliency detection algorithms usually show similarity imperfection against the ground truth, the authors propose an optimisation algorithm based on clustering and fitting (CF) for saliency detection. The algorithm uses a fitting model to represent the quantitative relationship between ground truth and algorithm-generated saliency maps. The authors use the K-means method to cluster the images into k clusters according to the similarities among images. Image similarity is measured in terms of scene and colour by using the GIST and colour histogram features, after which the fitting model for each cluster is calculated. The saliency map of a new image is optimised by using one of the fitting models which correspond to the cluster to which the image belongs. Experimental results show that their CF-based optimisation algorithm improves the performance of various single image saliency detection algorithms. Moreover, the improvement achieved by their algorithm when using both CF strategies is greater than the improvement achieved by the same algorithm when not using the clustering strategy. In addition, their proposed optimisation algorithm can also effectively optimise co-saliency detection algorithms which already consider multiple similar images simultaneously to improve saliency of single images.