Salient Object Detection: A Benchmark
[:en]
Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li
Notice: Welcome to contact Ming-Ming Cheng for adding new comparisons. Adding new results should supply either source code or executable.
Abstract
We extensively compare, qualitatively and quan- titatively, 42 state-of-the-art models (30 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection.
Papers
- Salient Object Detection: A Benchmark, Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li, IEEE TIP, 2015. [pdf] [Project page] [Bib]
- Salient Object Detection: A Survey, Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li, arXiv eprint, 2014. [pdf] [Project page] [Bib]
Code
- C++ & Matlab: Salient Object Detection: A Benchmark, IEEE TIP, 2015.
- More public available source code on this website.
Downloads
We provide the evaluation data (images, ground truth, saliency maps, etc.) downloads here to facilitate future research. We suggest to use BT software to download these zip files using their url lists, which is available here. Evaluation resutls in form of matlab and csv file for plots and talbes could be downloaded here. If you use any parts of our results, please cite the corresponding paper above. 您也可以通过百度网盘下载。
Performance: FMeasure of saliency maps and salient object segmentations
Max FMeasure of average precision recall curve, average FMeasure for adaptive thresholding results, average FMeasure for SalCut. The subtitle of each column is in the [Dataset]-[Evaluation Metric] format, where [Dataset] is represented by the initial letter for the 6 benchmarks {THUR15K, JuddDB, DUT-OMRON, SED2, MSRA10K, ECSSD}. Click the title of the column to rerank the table according to that metric.
Model T-M T-A T-S J-M J-A J-S D-M D-A D-S S-M S-A S-S M-M M-A M-S E-M E-A E-S
MBD .622 .594 .642 .472 .422 .470 .624 .592 .636 .799 .803 .759 .849 .830 .890 .739 .703 .785
ST .631 .580 .648 .455 .394 .459 .631 .577 .635 .818 .805 .768 .868 .825 .896 .752 .690 .777
QCUT .651 .625 .620 .509 .454 .480 .683 .647 .647 .810 .801 .672 .874 .843 .843 .779 .738 .747
HDCT .602 .571 .636 .412 .378 .422 .609 .572 .643 .822 .802 .758 .837 .807 .877 .705 .669 .74
RBD .596 .566 .618 .457 .403 .461 .63 .58 .647 .837 .825 .75 .856 .821 .884 .718 .68 .757
GR .551 .509 .546 .418 .338 .378 .599 .54 .58 .798 .753 .639 .816 .77 .83 .664 .583 .677
MNP .495 .523 .603 .367 .337 .405 .467 .486 .576 .621 .778 .765 .668 .724 .822 .568 .555 .709
UFO .579 .557 .61 .432 .385 .433 .545 .541 .593 .742 .781 .729 .842 .806 .862 .701 .654 .739
MC .61 .603 .6 .46 .42 .434 .627 .603 .615 .779 .803 .63 .847 .824 .855 .742 .704 .745
DSR .611 .604 .597 .454 .421 .41 .626 .614 .593 .794 .821 .632 .835 .824 .833 .737 .717 .703
CHM .612 .591 .643 .417 .368 .424 .604 .586 .637 .75 .75 .658 .825 .804 .857 .722 .684 .735
GC .533 .517 .497 .384 .321 .342 .535 .528 .506 .729 .73 .616 .794 .777 .78 .641 .612 .593
LBI .519 .534 .618 .371 .353 .416 .482 .504 .609 .692 .776 .764 .696 .714 .857 .586 .563 .738
PCA .544 .558 .601 .432 .404 .368 .554 .554 .624 .754 .796 .701 .782 .782 .845 .646 .627 .72
DRFI .67 .607 .674 .475 .419 .447 .665 .605 .669 .831 .839 .702 .881 .838 .905 .787 .733 .801
GMR .597 .594 .579 .454 .409 .432 .61 .591 .591 .773 .789 .643 .847 .825 .839 .74 .712 .736
HS .585 .549 .602 .442 .358 .428 .616 .565 .616 .811 .776 .713 .845 .8 .87 .731 .659 .769
LMLC .54 .519 .588 .375 .302 .397 .521 .493 .551 .653 .712 .674 .801 .772 .86 .659 .6 .735
SF .5 .495 .342 .373 .319 .219 .519 .512 .377 .764 .794 .509 .779 .759 .573 .619 .576 .378
FES .547 .575 .426 .424 .411 .333 .52 .555 .38 .617 .785 .174 .717 .753 .534 .645 .655 .467
CB .581 .556 .615 .444 .375 .435 .542 .534 .593 .73 .704 .657 .815 .775 .857 .717 .656 .761
SVO .554 .441 .609 .414 .279 .419 .557 .407 .609 .744 .667 .746 .789 .585 .863 .639 .357 .737
SWD .528 .56 .649 .434 .386 .454 .478 .506 .613 .548 .714 .737 .689 .705 .871 .624 .549 .781
HC .386 .401 .436 .286 .257 .28 .382 .38 .435 .736 .759 .646 .677 .663 .74 .46 .441 .499
RC .61 .586 .639 .431 .37 .425 .599 .578 .621 .774 .807 .649 .844 .82 .875 .741 .701 .776
SEG .5 .425 .58 .376 .268 .393 .516 .45 .562 .704 .64 .669 .697 .585 .812 .568 .408 .715
MSS .478 .49 .2 .341 .324 .089 .476 .49 .193 .743 .783 .298 .696 .711 .362 .53 .536 .203
CA .458 .494 .557 .353 .33 .394 .435 .458 .532 .591 .737 .565 .621 .679 .748 .515 .494 .625
FT .386 .4 .238 .278 .25 .132 .381 .388 .259 .715 .734 .436 .635 .628 .472 .434 .431 .257
AC .41 .431 .068 .227 .199 .049 .354 .383 .04 .684 .729 .14 .52 .566 .014 .411 .41 .038
LC .386 .408 .289 .264 .246 .156 .327 .353 .243 .683 .752 .486 .569 .589 .432 .39 .396 .219
OBJ .498 .482 .593 .368 .282 .413 .481 .445 .578 .685 .723 .731 .718 .681 .84 .574 .456 .698
BMS .568 .578 .594 .434 .404 .416 .573 .576 .58 .713 .76 .627 .805 .798 .822 .683 .659 .69
COV .51 .587 .398 .429 .427 .315 .486 .579 .373 .518 .724 .212 .667 .755 .394 .641 .677 .413
SS .415 .482 .523 .344 .321 .397 .396 .443 .502 .533 .696 .641 .572 .642 .675 .467 .441 .574
SIM .372 .429 .568 .295 .292 .384 .358 .402 .539 .498 .685 .725 .498 .585 .794 .433 .391 .672
SeR .374 .419 .536 .316 .285 .388 .385 .411 .532 .521 .714 .702 .542 .607 .755 .419 .391 .596
SUN .387 .432 .486 .303 .291 .285 .321 .36 .445 .504 .661 .613 .505 .596 .67 .388 .376 .478
SR .374 .457 .002 .279 .27 .001 .298 .363 0 .504 .7 .002 .473 .569 .001 .381 .385 .001
GB .526 .571 .65 .419 .396 .455 .507 .548 .638 .571 .746 .695 .688 .737 .837 .624 .613 .765
AIM .427 .461 .559 .317 .26 .36 .361 .377 .495 .541 .718 .693 .555 .575 .75 .449 .357 .571
IT .373 .437 .005 .297 .283 0 .378 .449 .005 .579 .697 .008 .471 .586 .158 .407 .414 .003
AVG .458 .569 .62 .392 .367 .411 .406 .514 .534 .388 .524 .64 .58 .692 .779 .597 .627 .756
Performance: AUC & MAE
Comparison of AUC scores (larger better) and MAE scores (smaller better). Similar to the table above, the subtitle of each column is in the [Dataset]-[Evaluation Metric] format, where [Dataset] is represented by the initial letter for the 6 benchmarks {THUR15K, JuddDB, DUT-OMRON, SED2, MSRA10K, ECSSD}. Click the title of the column to rerank the table according to that metric.
Method T-AUC T-MAE J-AUC J-MAE D-AUC D-MAE S-AUC S-MAE M-AUC M-MAE E-AUC E-MAE
MBD 0.915 0.162 0.838 0.225 0.903 0.168 0.922 0.137 0.964 0.107 0.917 0.172
ST 0.911 0.179 0.806 0.240 0.895 0.182 0.922 0.145 0.961 0.122 0.914 0.193
QCUT 0.907 0.128 0.831 0.178 0.897 0.119 0.860 0.148 0.956 0.118 0.909 0.171
HDCT 0.878 0.177 0.771 0.209 0.869 0.164 0.898 0.162 0.941 0.143 0.866 0.199
RBD 0.887 0.15 0.826 0.212 0.894 0.144 0.899 0.13 0.955 0.108 0.894 0.173
GR 0.829 0.256 0.747 0.311 0.846 0.259 0.854 0.189 0.925 0.198 0.831 0.285
MNP 0.854 0.255 0.768 0.286 0.835 0.272 0.888 0.215 0.895 0.229 0.82 0.307
UFO 0.853 0.165 0.775 0.216 0.839 0.173 0.845 0.18 0.938 0.15 0.875 0.207
MC 0.895 0.184 0.823 0.231 0.887 0.186 0.877 0.182 0.951 0.145 0.91 0.204
DSR 0.902 0.142 0.826 0.196 0.899 0.139 0.915 0.14 0.959 0.121 0.914 0.173
CHM 0.91 0.153 0.797 0.226 0.89 0.152 0.831 0.168 0.952 0.142 0.903 0.195
GC 0.803 0.192 0.702 0.258 0.796 0.197 0.846 0.185 0.912 0.139 0.805 0.214
LBI 0.876 0.239 0.792 0.273 0.854 0.249 0.896 0.207 0.91 0.224 0.842 0.28
PCA 0.885 0.198 0.804 0.181 0.887 0.206 0.911 0.2 0.941 0.185 0.876 0.248
DRFI 0.938 0.15 0.851 0.213 0.933 0.155 0.944 0.13 0.978 0.118 0.944 0.166
GMR 0.856 0.181 0.781 0.243 0.853 0.189 0.862 0.163 0.944 0.126 0.889 0.189
HS 0.853 0.218 0.775 0.282 0.86 0.227 0.858 0.157 0.933 0.149 0.883 0.228
LMLC 0.853 0.246 0.724 0.303 0.817 0.277 0.826 0.269 0.936 0.163 0.849 0.26
SF 0.799 0.184 0.711 0.218 0.803 0.183 0.871 0.18 0.905 0.175 0.817 0.23
FES 0.867 0.155 0.805 0.184 0.848 0.156 0.838 0.196 0.898 0.185 0.86 0.215
CB 0.87 0.227 0.76 0.287 0.831 0.257 0.839 0.195 0.927 0.178 0.875 0.241
SVO 0.865 0.382 0.784 0.422 0.866 0.409 0.875 0.348 0.93 0.331 0.857 0.404
SWD 0.873 0.288 0.812 0.292 0.843 0.31 0.845 0.296 0.901 0.267 0.857 0.318
HC 0.735 0.291 0.626 0.348 0.733 0.31 0.88 0.193 0.867 0.215 0.704 0.331
RC 0.896 0.168 0.775 0.27 0.859 0.189 0.852 0.148 0.936 0.137 0.892 0.187
SEG 0.818 0.336 0.747 0.354 0.825 0.337 0.796 0.312 0.882 0.298 0.808 0.342
MSS 0.813 0.178 0.726 0.204 0.817 0.177 0.871 0.192 0.875 0.203 0.779 0.245
CA 0.83 0.248 0.774 0.282 0.815 0.254 0.853 0.229 0.872 0.237 0.784 0.31
FT 0.684 0.241 0.593 0.267 0.682 0.25 0.82 0.206 0.79 0.235 0.661 0.291
AC 0.74 0.186 0.548 0.239 0.721 0.19 0.831 0.206 0.756 0.227 0.668 0.265
LC 0.696 0.229 0.586 0.277 0.654 0.246 0.827 0.204 0.771 0.233 0.627 0.296
OBJ 0.839 0.306 0.75 0.359 0.822 0.323 0.87 0.269 0.907 0.262 0.818 0.337
BMS 0.879 0.181 0.788 0.233 0.856 0.175 0.852 0.184 0.929 0.151 0.865 0.216
COV 0.883 0.155 0.826 0.182 0.864 0.156 0.833 0.21 0.904 0.197 0.879 0.217
SS 0.792 0.267 0.754 0.301 0.784 0.277 0.826 0.266 0.823 0.266 0.725 0.344
SIM 0.797 0.414 0.727 0.412 0.783 0.429 0.833 0.384 0.808 0.388 0.734 0.433
SeR 0.778 0.345 0.746 0.379 0.786 0.352 0.835 0.29 0.813 0.31 0.695 0.404
SUN 0.746 0.31 0.674 0.319 0.708 0.349 0.789 0.307 0.778 0.306 0.623 0.396
SR 0.741 0.175 0.676 0.2 0.688 0.181 0.769 0.22 0.736 0.232 0.633 0.266
GB 0.882 0.229 0.815 0.261 0.857 0.24 0.839 0.242 0.902 0.222 0.865 0.263
AIM 0.814 0.298 0.719 0.331 0.768 0.322 0.846 0.262 0.833 0.286 0.73 0.339
IT 0.623 0.199 0.586 0.2 0.636 0.198 0.682 0.245 0.64 0.213 0.577 0.273
AVG 0.849 0.248 0.797 0.343 0.814 0.288 0.736 0.405 0.857 0.26 0.863 0.276
Salient object detection datasets
Abbr. Images References
MSRA10K 10000 Learning to Detect A Salient Object , IEEE CVPR 2007, Liu et al. Frequency-tuned Salient Region Detection, IEEE CVPR 2009, Achanta et al. Global Contrast based Salient Region Detection, IEEE TPAMI 2015, Cheng et al.
ECSSD 1000 Hierarchical Saliency Detection, IEEE CVPR 2013, Yan et al.
THUR15K 15000 SalientShape: Group Saliency in Image Collections, The Visual Computer 2013, Cheng et al.
JuddDB 900 What is a salient object? A dataset and a baseline model for salient object detection, arXiv, ePrints
DUTOMRON 5000 Saliency Detection Via Graph-Based Manifold Ranking, IEEE CVPR 2013, Yang et al.
SED 2 100 Image segmentation by probabilistic
bottom-up aggregation and cue integration, IEEE CVPR 2007, Alpert et alInformation about different methods
Detailed information of each method. Regarding source code type: `C' means 'C/C++', 'M' means 'Matlab', 'M+C' means a mixture of Matlab and C/C++.
News
- 2015/4/24: evaluation results of QCUT has been added.
- 2015/10/24: evaluation results of MBD has been added.
[:zh]
Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li
Notice: Welcome to contact Ming-Ming Cheng for adding new comparisons. Adding new results should supply either source code or executable.
Abstract
We extensively compare, qualitatively and quan- titatively, 42 state-of-the-art models (30 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection.
Papers
- Salient Object Detection: A Benchmark, Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li, IEEE TIP, 2015. [pdf] [Project page] [Bib]
- Salient Object Detection: A Survey, Ali Borji, Ming-Ming Cheng, Huaizu Jiang, Jia Li, arXiv eprint, 2014. [pdf] [Project page] [Bib]
Code
- C++ & Matlab: Salient Object Detection: A Benchmark, IEEE TIP, 2015.
- More public available source code on this website.
Downloads
We provide the evaluation data (images, ground truth, saliency maps, etc.) to facilitate future research. Link: 百度网盘。
Performance: FMeasure of saliency maps and salient object segmentations
Max FMeasure of average precision recall curve, average FMeasure for adaptive thresholding results, average FMeasure for SalCut. The subtitle of each column is in the [Dataset]-[Evaluation Metric] format, where [Dataset] is represented by the initial letter for the 6 benchmarks {THUR15K, JuddDB, DUT-OMRON, SED2, MSRA10K, ECSSD}. Click the title of the column to rerank the table according to that metric.
Model T-M T-A T-S J-M J-A J-S D-M D-A D-S S-M S-A S-S M-M M-A M-S E-M E-A E-S
MBD .622 .594 .642 .472 .422 .470 .624 .592 .636 .799 .803 .759 .849 .830 .890 .739 .703 .785
ST .631 .580 .648 .455 .394 .459 .631 .577 .635 .818 .805 .768 .868 .825 .896 .752 .690 .777
QCUT .651 .625 .620 .509 .454 .480 .683 .647 .647 .810 .801 .672 .874 .843 .843 .779 .738 .747
HDCT .602 .571 .636 .412 .378 .422 .609 .572 .643 .822 .802 .758 .837 .807 .877 .705 .669 .74
RBD .596 .566 .618 .457 .403 .461 .63 .58 .647 .837 .825 .75 .856 .821 .884 .718 .68 .757
GR .551 .509 .546 .418 .338 .378 .599 .54 .58 .798 .753 .639 .816 .77 .83 .664 .583 .677
MNP .495 .523 .603 .367 .337 .405 .467 .486 .576 .621 .778 .765 .668 .724 .822 .568 .555 .709
UFO .579 .557 .61 .432 .385 .433 .545 .541 .593 .742 .781 .729 .842 .806 .862 .701 .654 .739
MC .61 .603 .6 .46 .42 .434 .627 .603 .615 .779 .803 .63 .847 .824 .855 .742 .704 .745
DSR .611 .604 .597 .454 .421 .41 .626 .614 .593 .794 .821 .632 .835 .824 .833 .737 .717 .703
CHM .612 .591 .643 .417 .368 .424 .604 .586 .637 .75 .75 .658 .825 .804 .857 .722 .684 .735
GC .533 .517 .497 .384 .321 .342 .535 .528 .506 .729 .73 .616 .794 .777 .78 .641 .612 .593
LBI .519 .534 .618 .371 .353 .416 .482 .504 .609 .692 .776 .764 .696 .714 .857 .586 .563 .738
PCA .544 .558 .601 .432 .404 .368 .554 .554 .624 .754 .796 .701 .782 .782 .845 .646 .627 .72
DRFI .67 .607 .674 .475 .419 .447 .665 .605 .669 .831 .839 .702 .881 .838 .905 .787 .733 .801
GMR .597 .594 .579 .454 .409 .432 .61 .591 .591 .773 .789 .643 .847 .825 .839 .74 .712 .736
HS .585 .549 .602 .442 .358 .428 .616 .565 .616 .811 .776 .713 .845 .8 .87 .731 .659 .769
LMLC .54 .519 .588 .375 .302 .397 .521 .493 .551 .653 .712 .674 .801 .772 .86 .659 .6 .735
SF .5 .495 .342 .373 .319 .219 .519 .512 .377 .764 .794 .509 .779 .759 .573 .619 .576 .378
FES .547 .575 .426 .424 .411 .333 .52 .555 .38 .617 .785 .174 .717 .753 .534 .645 .655 .467
CB .581 .556 .615 .444 .375 .435 .542 .534 .593 .73 .704 .657 .815 .775 .857 .717 .656 .761
SVO .554 .441 .609 .414 .279 .419 .557 .407 .609 .744 .667 .746 .789 .585 .863 .639 .357 .737
SWD .528 .56 .649 .434 .386 .454 .478 .506 .613 .548 .714 .737 .689 .705 .871 .624 .549 .781
HC .386 .401 .436 .286 .257 .28 .382 .38 .435 .736 .759 .646 .677 .663 .74 .46 .441 .499
RC .61 .586 .639 .431 .37 .425 .599 .578 .621 .774 .807 .649 .844 .82 .875 .741 .701 .776
SEG .5 .425 .58 .376 .268 .393 .516 .45 .562 .704 .64 .669 .697 .585 .812 .568 .408 .715
MSS .478 .49 .2 .341 .324 .089 .476 .49 .193 .743 .783 .298 .696 .711 .362 .53 .536 .203
CA .458 .494 .557 .353 .33 .394 .435 .458 .532 .591 .737 .565 .621 .679 .748 .515 .494 .625
FT .386 .4 .238 .278 .25 .132 .381 .388 .259 .715 .734 .436 .635 .628 .472 .434 .431 .257
AC .41 .431 .068 .227 .199 .049 .354 .383 .04 .684 .729 .14 .52 .566 .014 .411 .41 .038
LC .386 .408 .289 .264 .246 .156 .327 .353 .243 .683 .752 .486 .569 .589 .432 .39 .396 .219
OBJ .498 .482 .593 .368 .282 .413 .481 .445 .578 .685 .723 .731 .718 .681 .84 .574 .456 .698
BMS .568 .578 .594 .434 .404 .416 .573 .576 .58 .713 .76 .627 .805 .798 .822 .683 .659 .69
COV .51 .587 .398 .429 .427 .315 .486 .579 .373 .518 .724 .212 .667 .755 .394 .641 .677 .413
SS .415 .482 .523 .344 .321 .397 .396 .443 .502 .533 .696 .641 .572 .642 .675 .467 .441 .574
SIM .372 .429 .568 .295 .292 .384 .358 .402 .539 .498 .685 .725 .498 .585 .794 .433 .391 .672
SeR .374 .419 .536 .316 .285 .388 .385 .411 .532 .521 .714 .702 .542 .607 .755 .419 .391 .596
SUN .387 .432 .486 .303 .291 .285 .321 .36 .445 .504 .661 .613 .505 .596 .67 .388 .376 .478
SR .374 .457 .002 .279 .27 .001 .298 .363 0 .504 .7 .002 .473 .569 .001 .381 .385 .001
GB .526 .571 .65 .419 .396 .455 .507 .548 .638 .571 .746 .695 .688 .737 .837 .624 .613 .765
AIM .427 .461 .559 .317 .26 .36 .361 .377 .495 .541 .718 .693 .555 .575 .75 .449 .357 .571
IT .373 .437 .005 .297 .283 0 .378 .449 .005 .579 .697 .008 .471 .586 .158 .407 .414 .003
AVG .458 .569 .62 .392 .367 .411 .406 .514 .534 .388 .524 .64 .58 .692 .779 .597 .627 .756
Performance: AUC & MAE
Comparison of AUC scores (larger better) and MAE scores (smaller better). Similar to the table above, the subtitle of each column is in the [Dataset]-[Evaluation Metric] format, where [Dataset] is represented by the initial letter for the 6 benchmarks {THUR15K, JuddDB, DUT-OMRON, SED2, MSRA10K, ECSSD}. Click the title of the column to rerank the table according to that metric.
Method T-AUC T-MAE J-AUC J-MAE D-AUC D-MAE S-AUC S-MAE M-AUC M-MAE E-AUC E-MAE
MBD 0.915 0.162 0.838 0.225 0.903 0.168 0.922 0.137 0.964 0.107 0.917 0.172
ST 0.911 0.179 0.806 0.240 0.895 0.182 0.922 0.145 0.961 0.122 0.914 0.193
QCUT 0.907 0.128 0.831 0.178 0.897 0.119 0.860 0.148 0.956 0.118 0.909 0.171
HDCT 0.878 0.177 0.771 0.209 0.869 0.164 0.898 0.162 0.941 0.143 0.866 0.199
RBD 0.887 0.15 0.826 0.212 0.894 0.144 0.899 0.13 0.955 0.108 0.894 0.173
GR 0.829 0.256 0.747 0.311 0.846 0.259 0.854 0.189 0.925 0.198 0.831 0.285
MNP 0.854 0.255 0.768 0.286 0.835 0.272 0.888 0.215 0.895 0.229 0.82 0.307
UFO 0.853 0.165 0.775 0.216 0.839 0.173 0.845 0.18 0.938 0.15 0.875 0.207
MC 0.895 0.184 0.823 0.231 0.887 0.186 0.877 0.182 0.951 0.145 0.91 0.204
DSR 0.902 0.142 0.826 0.196 0.899 0.139 0.915 0.14 0.959 0.121 0.914 0.173
CHM 0.91 0.153 0.797 0.226 0.89 0.152 0.831 0.168 0.952 0.142 0.903 0.195
GC 0.803 0.192 0.702 0.258 0.796 0.197 0.846 0.185 0.912 0.139 0.805 0.214
LBI 0.876 0.239 0.792 0.273 0.854 0.249 0.896 0.207 0.91 0.224 0.842 0.28
PCA 0.885 0.198 0.804 0.181 0.887 0.206 0.911 0.2 0.941 0.185 0.876 0.248
DRFI 0.938 0.15 0.851 0.213 0.933 0.155 0.944 0.13 0.978 0.118 0.944 0.166
GMR 0.856 0.181 0.781 0.243 0.853 0.189 0.862 0.163 0.944 0.126 0.889 0.189
HS 0.853 0.218 0.775 0.282 0.86 0.227 0.858 0.157 0.933 0.149 0.883 0.228
LMLC 0.853 0.246 0.724 0.303 0.817 0.277 0.826 0.269 0.936 0.163 0.849 0.26
SF 0.799 0.184 0.711 0.218 0.803 0.183 0.871 0.18 0.905 0.175 0.817 0.23
FES 0.867 0.155 0.805 0.184 0.848 0.156 0.838 0.196 0.898 0.185 0.86 0.215
CB 0.87 0.227 0.76 0.287 0.831 0.257 0.839 0.195 0.927 0.178 0.875 0.241
SVO 0.865 0.382 0.784 0.422 0.866 0.409 0.875 0.348 0.93 0.331 0.857 0.404
SWD 0.873 0.288 0.812 0.292 0.843 0.31 0.845 0.296 0.901 0.267 0.857 0.318
HC 0.735 0.291 0.626 0.348 0.733 0.31 0.88 0.193 0.867 0.215 0.704 0.331
RC 0.896 0.168 0.775 0.27 0.859 0.189 0.852 0.148 0.936 0.137 0.892 0.187
SEG 0.818 0.336 0.747 0.354 0.825 0.337 0.796 0.312 0.882 0.298 0.808 0.342
MSS 0.813 0.178 0.726 0.204 0.817 0.177 0.871 0.192 0.875 0.203 0.779 0.245
CA 0.83 0.248 0.774 0.282 0.815 0.254 0.853 0.229 0.872 0.237 0.784 0.31
FT 0.684 0.241 0.593 0.267 0.682 0.25 0.82 0.206 0.79 0.235 0.661 0.291
AC 0.74 0.186 0.548 0.239 0.721 0.19 0.831 0.206 0.756 0.227 0.668 0.265
LC 0.696 0.229 0.586 0.277 0.654 0.246 0.827 0.204 0.771 0.233 0.627 0.296
OBJ 0.839 0.306 0.75 0.359 0.822 0.323 0.87 0.269 0.907 0.262 0.818 0.337
BMS 0.879 0.181 0.788 0.233 0.856 0.175 0.852 0.184 0.929 0.151 0.865 0.216
COV 0.883 0.155 0.826 0.182 0.864 0.156 0.833 0.21 0.904 0.197 0.879 0.217
SS 0.792 0.267 0.754 0.301 0.784 0.277 0.826 0.266 0.823 0.266 0.725 0.344
SIM 0.797 0.414 0.727 0.412 0.783 0.429 0.833 0.384 0.808 0.388 0.734 0.433
SeR 0.778 0.345 0.746 0.379 0.786 0.352 0.835 0.29 0.813 0.31 0.695 0.404
SUN 0.746 0.31 0.674 0.319 0.708 0.349 0.789 0.307 0.778 0.306 0.623 0.396
SR 0.741 0.175 0.676 0.2 0.688 0.181 0.769 0.22 0.736 0.232 0.633 0.266
GB 0.882 0.229 0.815 0.261 0.857 0.24 0.839 0.242 0.902 0.222 0.865 0.263
AIM 0.814 0.298 0.719 0.331 0.768 0.322 0.846 0.262 0.833 0.286 0.73 0.339
IT 0.623 0.199 0.586 0.2 0.636 0.198 0.682 0.245 0.64 0.213 0.577 0.273
AVG 0.849 0.248 0.797 0.343 0.814 0.288 0.736 0.405 0.857 0.26 0.863 0.276
Salient object detection datasets
Abbr. Images References
MSRA10K 10000 Learning to Detect A Salient Object , IEEE CVPR 2007, Liu et al. Frequency-tuned Salient Region Detection, IEEE CVPR 2009, Achanta et al. Global Contrast based Salient Region Detection, IEEE TPAMI 2015, Cheng et al.
ECSSD 1000 Hierarchical Saliency Detection, IEEE CVPR 2013, Yan et al.
THUR15K 15000 SalientShape: Group Saliency in Image Collections, The Visual Computer 2013, Cheng et al.
JuddDB 900 What is a salient object? A dataset and a baseline model for salient object detection, arXiv, ePrints
DUTOMRON 5000 Saliency Detection Via Graph-Based Manifold Ranking, IEEE CVPR 2013, Yang et al.
SED 2 100 Image segmentation by probabilistic
bottom-up aggregation and cue integration, IEEE CVPR 2007, Alpert et alInformation about different methods
Detailed information of each method. Regarding source code type: `C' means 'C/C++', 'M' means 'Matlab', 'M+C' means a mixture of Matlab and C/C++.
News
- 2015/4/24: evaluation results of QCUT has been added
- 2015/10/24: evaluation results of MBD has been added
[:]