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2018 | OriginalPaper | Chapter

Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

Authors : Deng-Ping Fan, Ming-Ming Cheng, Jiang-Jiang Liu, Shang-Hua Gao, Qibin Hou, Ali Borji

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

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Abstract

We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-of-the-art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our SOC (Salient Objects in Clutter) dataset, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes. Finally, we report attribute-based performance assessment on our dataset.

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previous chapter Learning Blind Video Temporal Consistency

next chapter Gray-Box Adversarial Training

http://dpfan.net/SOCBenchmark/.

Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned Salient Region Detection. In: CVPR, pp. 1597–1604. IEEE (2009)

Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPR, pp. 1–8. IEEE (2007)

Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706–5722 (2015)MathSciNetCrossRef

Borji, A., Itti, L.: State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 185–207 (2013)CrossRef

Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 414–429. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33709-3_30CrossRef

Caesar, H., Uijlings, J., Ferrari, V.: COCO-stuff: thing and stuff classes in context. In: CVPR. IEEE (2018)

Chen, T., Cheng, M.M., Tan, P., Shamir, A., Hu, S.M.: Sketch2photo: internet image montage. ACM Trans. Graph. (TOG) 28(5), 124 (2009)

Chen, T., Lin, L., Liu, L., Luo, X., Li, X.: DISC: deep image saliency computing via progressive representation learning. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1135–1149 (2016)MathSciNetCrossRef

Cheng, M.M., Hou, Q.B., Zhang, S.H., Rosin, P.L.: Intelligent visual media processing: when graphics meets vision. J. Comput. Sci. Technol. 32(1), 110–121 (2017)CrossRef

10.

Cheng, M.M., Mitra, N.J., Huang, X., Hu, S.M.: Salientshape: group saliency in image collections. Vis. Comput. 30(4), 443–453 (2014)CrossRef

11.

Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)CrossRef

12.

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2010 (VOC 2010) Results. https://www.researchgate.net/profile/Luc_Van_Gool/publication/277292831_The_2005_pascal_visual_object_classes_challenge/links/57224cf108aef9c00b7c7efb.pdf

13.

Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: ICCV, pp. 4548–4557. IEEE (2017)

14.

Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., Borji, A.: Enhanced-alignment measure for binary foreground map evaluation. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 698–704 (2018)

15.

Gayoung, L., Yu-Wing, T., Junmo, K.: Deep saliency with encoded low level distance map and high level features. In: CVPR, IEEE (2016)

16.

He, J. et al.: Mobile product search with bag of hash bits and boundary reranking. In: CVPR, pp. 3005–3012. IEEE (2012)

17.

Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., Torr, P.: Deeply supervised salient object detection with short connections. IEEE TPAMI (2018). https://doi.org/10.1109/TPAMI.2018.2815688

18.

Hou, Q., Dokania, P.K., Massiceti, D., Wei, Y., Cheng, M.M., Torr, P.H.S.: Bottom-up top-down cues for weakly supervised semantic segmentation. In: EMMCVPR. IEEE (2017)

19.

Jiang, H., Cheng, M.M., Li, S.J., Borji, A., Wang, J.: Joint salient object detection and existence prediction. Front. Comput. Sci., 1–11 (2017)

20.

Judd, T., Durand, F., Torralba, A.: A benchmark of computational models of saliency to predict human fixations. In: MIT Technical Report (2012)

21.

Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE TPAMI 27(8), 1265–1278 (2005)CrossRef

22.

Li, G., Xie, Y., Lin, L., Yu, Y.: Instance-level salient object segmentation. In: CVPR, pp. 247–256. IEEE (2017)

23.

Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: CVPR, pp. 5455–5463. IEEE (2015)

24.

Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: CVPR, pp. 478–487. IEEE (2016)

25.

Li, X., Zhao, L., Wei, L., Yang, M.H., Wu, F., Zhuang, Y., Ling, H., Wang, J.: DeepSaliency: multi-task deep neural network model for salient object detection. IEEE TIP 25(8), 3919–3930 (2016)MathSciNet

26.

Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: CVPR, pp. 280–287. IEEE (2014)

27.

Lin, T.-Y. et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

28.

Liu, N., Han, J.: DHSNet: deep hierarchical saliency network for salient object detection. In: CVPR, pp. 678–686. IEEE (2016)

29.

Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: CVPR, pp. 1–8. IEEE (2007)

30.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440. IEEE (2015)

31.

Luo, Z., Mishra, A.K., Achkar, A., Eichel, J.A., Li, S., Jodoin, P.M.: Non-local deep features for salient object detection. In: CVPR, vol. 2, p. 7 (2017)

32.

Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, vol. 2, pp. 416–423. IEEE (2001)

33.

Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR, pp. 724–732. IEEE (2016)

34.

Russakovsky, O. et al.: Imagenet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRef

35.

Wang, J., Jiang, H., Yuan, Z., Cheng, M.M., Hu, X., Zheng, N.: Salient object detection: a discriminative regional feature integration approach. IJCV 123(2), 251–268 (2017)CrossRef

36.

Wang, L., Lu, H., Ruan, X., Yang, M.H.: Deep networks for saliency detection via local estimation and global search. In: CVPR, pp. 3183–3192. IEEE (2015)

37.

Wang, L. et al.: Learning to detect salient objects with image-level supervision. In: CVPR, pp. 136–145. IEEE (2017)

38.

Wang, L., Wang, L., Lu, H., Zhang, P., Ruan, X.: Saliency detection with recurrent fully convolutional networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 825–841. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_50CrossRef

39.

Wei, Y., Feng, J., Liang, X., Cheng, M.M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: CVPR. IEEE (2017)

40.

Wei, Y.: STC: A simple to complex framework for weakly-supervised semantic segmentation. IEEE TPAMI 39(11), 2314–2320 (2017)CrossRef

41.

Xia, C., Li, J., Chen, X., Zheng, A., Zhang, Y.: What is and what is not a salient object? Learning salient object detector by ensembling linear exemplar regressors. In: CVPR. IEEE (2017)

42.

Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV, pp. 1395–1403. IEEE (2015)

43.

Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR, pp. 1155–1162. IEEE (2013)

44.

Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: CVPR, pp. 3166–3173. IEEE (2013)

45.

Zhang, G.X., Cheng, M.M., Hu, S.M., Martin, R.R.: A shape-preserving approach to image resizing. Comput. Graph. Forum 28(7), 1897–1906 (2009)CrossRef

46.

Zhang, J. et al.: Salient object subitizing. In: CVPR, pp. 4045–4054. IEEE (2015)

47.

Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Unconstrained salient object detection via proposal subset optimization. In: CVPR, pp. 5733–5742. IEEE (2016)

48.

Zhang, J., Dai, Y., Porikli, F.: Deep salient object detection by integrating multi-level cues. In: Winter Conference on Applications of Computer Vision (WACV), pp. 1–10. IEEE (2017)

49.

Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: ICCV, pp. 202–211 (2017)

50.

Zhang, P., Wang, D., Lu, H., Wang, H., Yin, B.: Learning uncertain convolutional features for accurate saliency detection. In: ICCV, pp. 212–221 (2017)

51.

Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: CVPR, pp. 1265–1274. IEEE (2015)

Title: Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
Authors: Deng-Ping Fan
Ming-Ming Cheng
Jiang-Jiang Liu
Shang-Hua Gao
Qibin Hou
Ali Borji
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01266-3

Electronic ISBN: 978-3-030-01267-0

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01267-0_12

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