Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (4): 1-7.doi: 10.6040/j.issn.1672-3961.0.2020.266

• Machine Learning & Data Mining •     Next Articles

Image-dependent fusion method for saliency maps

Ye LIANG1,2(),Nan MA2,Hongzhe LIU1   

  1. 1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
    2. College of Robotics, Beijing Union University, Beijing 100044, China
  • Received:2020-03-31 Online:2021-08-20 Published:2021-08-18

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Abstract:

A saliency fusion method based on ridge regression was proposed to obtain better detection performance. The nearest neighbor set of the image to be detected was searched in the training set. The ridge regression method was used to estimate the fusion coefficients of different saliency maps. The saliency maps of different detection methods were fused. This method fully considered the differences of detection methods, and solved the problem of saliency map fusion in the absence of benchmark binary annotations. The AUC value of the proposed method was 0.911 on ECSSD dataset. The AUC value of the proposed method was 0.987 on HKU-IS dataset. The AUC value of the proposed method was 0.953 on DUT-OMRON dataset. The efficiency of the proposed method was verified by experimental results.

Key words: visual attention mechanism, saliency detection, saliency fusion, image-dependence, ridge regression

CLC Number: 

  • TP391.41

Fig.1

Some detection results of different methods"

Table 1

Detection results on ECSSD"

方法 F-measure AUC MAE
MR 0.637 0.834 0.237
BSCA 0.658 0.845 0.233
MBS 0.634 0.826 0.231
MC 0.628 0.837 0.251
SELD 0.769 0.866 0.156
DCL 0.699 0.889 0.162
DS 0.717 0.894 0.189
MDF 0.729 0.859 0.174
指数融合方法 0.718 0.901 0.199
线性融合方法 0.598 0.734 0.313
全局融合方法 0.659 0.875 0.332
本研究方法 0.777 0.911 0.171

Table 2

Detection results on HKU-IS"

方法 F-measure AUC MAE
MR 0.645 0.867 0.188
BSCA 0.661 0.910 0.175
MBS 0.681 0.920 0.174
MC 0.651 0.909 0.184
SELD 0.799 0.958 0.074
DCL 0.748 0.977 0.072
DS 0.811 0.981 0.079
MDF 0.770 0.969 0.129
指数融合方法 0.773 0.980 0.126
线性融合方法 0.531 0.808 0.309
全局融合方法 0.758 0.915 0.183
本文方法 0.818 0.987 0.071

Table 3

Detection results on DUT-OMRON"

方法 F-measure AUC MAE
MR 0.406 0.727 0.246
BSCA 0.574 0.865 0.192
MBS 0.576 0.868 0.157
MC 0.403 0.771 0.238
SELD 0.677 0.914 0.092
DCL 0.629 0.922 0.097
DS 0.675 0.940 0.120
MDF 0.663 0.898 0.093
指数融合方法 0.677 0.943 0.129
线性融合方法 0.448 0.759 0.370
全局融合方法 0.567 0.863 0.239
本文方法 0.688 0.953 0.092

Table 4

Detection results on ECSSD"

方法 Smeasure wFmeasure Emeasure
MR 0.639 0.476 0.599
BSCA 0.668 0.495 0.627
MBS 0.632 0.499 0.648
MC 0.640 0.441 0.579
SELD 0.760 0.717 0.805
DCL 0.784 0.698 0.769
DS 0.746 0.618 0.687
MDF 0.712 0.653 0.735
本文方法 0.786 0.721 0.787

Table 5

Detection results on HKU-IS"

方法 Smeasure wFmeasure Emeasure
MR 0.669 0.444 0.622
BSCA 0.702 0.467 0.658
MBS 0.714 0.493 0.697
MC 0.687 0.423 0.612
SELD 0.820 0.742 0.876
DCL 0.860 0.739 0.840
DS 0.852 0.709 0.846
MDF 0.810 0.564 0.739
本文方法 0.863 0.736 0.880

Table 6

Detection results on DUT-OMRON"

方法 Smeasure wFmeasure Emeasure
MR 0.645 0.380 0.626
BSCA 0.652 0.370 0.629
MBS 0.651 0.419 0.678
MC 0.649 0.347 0.610
SELD 0.750 0.592 0.789
DCL 0.764 0.575 0.750
DS 0.750 0.487 0.704
MDF 0.721 0.565 0.758
本文方法 0.758 0.597 0.793

Fig.2

Detection results on ECCSD"

Fig.3

Detection results on HKU-IS"

Fig.4

Detection results on DUT-OMRON"

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