Fitting-based optimisation for image visual salient object detection

To overcome some major problems with traditional saliency evaluation metrics, full-reference image quality assessment (IQA) metrics, which have similar but stricter objectives, are used. Inspired by the root mean absolute error, the authors propose a fitting-based optimisation method for salient object detection algorithms. Their algorithm analyses the quantitative relationship between saliency and ground truth values, and uses the derived relationship to fit the saliency values to the original saliency maps. This ensures that the resulting images, which are composed of fitted values, are closer to the ground truth. The proposed algorithm first computes the statistics of the ground truth and saliency maps computed by each salient object detection algorithm. These statistics are used to compute the parameters of four fitting models, which generally agree with the characteristics of the statistical data. For a new saliency map, they use the fitting model with the computed parameters to obtain the fitted saliency values, which are confined to the range [0, 255]. Finally, they evaluate their saliency optimisation algorithm using traditional evaluation metrics, IQA metrics, and a content-based image retrieval application. The results show that the proposed approach improves the quality of the optimised saliency maps.