Journal
IET COMPUTER VISION
Volume 11, Issue 2, Pages 161-172Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-cvi.2016.0027
Keywords
object detection; optimisation; statistical analysis; content-based retrieval; image retrieval; fitting-based optimisation method; image visual salient object detection; full-reference image quality assessment metrics; root mean absolute error; ground truth value; saliency value; saliency maps; statistics computation; saliency optimisation algorithm; content-based image retrieval application; IQA metrics
Funding
- National Natural Science Foundation of China [61300102, 61672158, 61502105]
- Fujian Natural Science Funds for Distinguished Young Scholar [2015J06014]
- Natural Science Foundation of Fujian Province [2014J01233]
- Natural Science Foundation of Fujian Provincial Education Department [JA15075]
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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.
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