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access icon free Fitting-based optimisation for image visual salient object detection

  • Author(s): Yuzhen Niu 1, 2 ; Wenqi Lin 2 ; Xiao Ke 1, 2 ; Lingling Ke 2
    • View affiliations
    • Affiliations: 1: Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108, People's Republic of China ;
      2: College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350108, People's Republic of China
  • Source: Volume 11, Issue 2, March 2017, p. 161 – 172
    DOI:  10.1049/iet-cvi.2016.0027 , Print ISSN 1751-9632, Online ISSN 1751-9640
© The Institution of Engineering and Technology
Received 26/01/2016, Accepted 29/06/2016, Revised 15/06/2016, Published 01/03/2017

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.

Inspec keywords: content-based retrieval; object detection; statistical analysis; image retrieval; optimisation

Other keywords: saliency value; saliency maps; saliency optimisation algorithm; statistics computation; root mean absolute error; IQA metrics; full-reference image quality assessment metrics; ground truth value; image visual salient object detection; content-based image retrieval application; fitting-based optimisation method

Subjects: Other topics in statistics; Other topics in statistics; Optimisation techniques; Information retrieval techniques; Optical, image and video signal processing; Computer vision and image processing techniques; Optimisation techniques

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