{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:04:38Z","timestamp":1740107078822,"version":"3.37.3"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4182022"],"award-info":[{"award-number":["4182022"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871038","61871039"],"award-info":[{"award-number":["61871038","61871039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s00371-019-01781-9","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T16:03:12Z","timestamp":1575907392000},"page":"1883-1895","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A novel deep network and aggregation model for saliency detection"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1715-4538","authenticated-orcid":false,"given":"Ye","family":"Liang","sequence":"first","affiliation":[]},{"given":"Hongzhe","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,9]]},"reference":[{"key":"1781_CR1","first-page":"1","volume":"35","author":"C Yang","year":"2018","unstructured":"Yang, C., Pu, J., Dong, Y., Xie, G.-S., Si, Y., Liu, Z.: Scene classification-oriented saliency detection via the modularized prescription. Vis. Comput. 35, 1\u201316 (2018)","journal-title":"Vis. Comput."},{"key":"1781_CR2","first-page":"1","volume":"35","author":"X Zhou","year":"2018","unstructured":"Zhou, X., Wang, Y., Zhu, Q., Xiao, C., Lu, X.: Ssg: superpixel segmentation and grabcut-based salient object segmentation. Vis. Comput. 35, 1\u201314 (2018)","journal-title":"Vis. Comput."},{"issue":"11","key":"1781_CR3","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1109\/TPAMI.2016.2636150","volume":"39","author":"Y Wei","year":"2016","unstructured":"Wei, Y., Liang, X., Chen, Y., Shen, X., Cheng, M.-M., Feng, J., Zhao, Y., Yan, S.: Stc: a simple to complex framework for weakly-supervised semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2314\u20132320 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1781_CR4","doi-asserted-by":"crossref","unstructured":"Guo, J., Ren, T., Huang, L., Liu, X., Cheng, M.-M., Wu, G.: Video salient object detection via cross-frame cellular automata. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 325\u2013330. IEEE (2017)","DOI":"10.1109\/ICME.2017.8019389"},{"key":"1781_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, M.-M., Zhang, F.-L., Mitra, N.J., Huang, X., Hu, S.-M.: Repfinder: finding approximately repeated scene elements for image editing. In: ACM Transactions on Graphics (TOG), vol.\u00a029, no. 4, p. 83. ACM (2010)","DOI":"10.1145\/1778765.1778820"},{"issue":"1","key":"1781_CR6","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s11390-017-1681-7","volume":"32","author":"M-M Cheng","year":"2017","unstructured":"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\u2013121 (2017)","journal-title":"J. Comput. Sci. Technol."},{"key":"1781_CR7","doi-asserted-by":"crossref","unstructured":"Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2376\u20132383 (2010)","DOI":"10.1109\/CVPR.2010.5539929"},{"issue":"2","key":"1781_CR8","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1109\/TPAMI.2010.70","volume":"33","author":"T Liu","year":"2011","unstructured":"Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 353\u2013367 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1781_CR9","doi-asserted-by":"crossref","unstructured":"Cheng, M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.: Global contrast based salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 409\u2013416 (2011)","DOI":"10.1109\/CVPR.2011.5995344"},{"issue":"9","key":"1781_CR10","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/LSP.2014.2323407","volume":"21","author":"N Tong","year":"2014","unstructured":"Tong, N., Lu, H., Zhang, L., Ruan, X.: Saliency detection with multi-scale superpixels. IEEE Signal Process. Lett. 21(9), 1035\u20131039 (2014)","journal-title":"IEEE Signal Process. Lett."},{"key":"1781_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00371-018-01620-3","volume":"35","author":"Y Lu","year":"2019","unstructured":"Lu, Y., Zhou, K., Wu, X., Gong, P.: A novel multi-graph framework for salient object detection. Vis. Comput. 35, 1\u201317 (2019)","journal-title":"Vis. Comput."},{"issue":"5","key":"1781_CR12","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00371-017-1404-7","volume":"34","author":"B Wang","year":"2018","unstructured":"Wang, B., Zhang, T., Wang, X.: Salient object detection based on Laplacian similarity metrics. Vis. Comput. 34(5), 645\u2013658 (2018)","journal-title":"Vis. Comput."},{"issue":"5","key":"1781_CR13","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00371-016-1216-1","volume":"33","author":"A Kapoor","year":"2017","unstructured":"Kapoor, A., Biswas, K., Hanmandlu, M.: An evolutionary learning based fuzzy theoretic approach for salient object detection. Vis. Comput. 33(5), 665\u2013685 (2017)","journal-title":"Vis. Comput."},{"issue":"11","key":"1781_CR14","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1007\/s00371-016-1287-z","volume":"33","author":"Z Yang","year":"2017","unstructured":"Yang, Z., Xiong, H.: Computing object-based saliency via locality-constrained linear coding and conditional random fields. Vis. Comput. 33(11), 1403\u20131413 (2017)","journal-title":"Vis. Comput."},{"issue":"11","key":"1781_CR15","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1007\/s00371-016-1288-y","volume":"33","author":"C Yang","year":"2017","unstructured":"Yang, C., Pu, J., Dong, Y., Liu, Z., Liang, L., Wang, X.: Salient object detection in complex scenes via ds evidence theory based region classification. Vis. Comput. 33(11), 1415\u20131428 (2017)","journal-title":"Vis. Comput."},{"issue":"9","key":"1781_CR16","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1007\/s00371-016-1278-0","volume":"33","author":"R Li","year":"2017","unstructured":"Li, R., Cai, J., Zhang, H., Wang, T.: Aggregating complementary boundary contrast with smoothing for salient region detection. Vis. Comput. 33(9), 1155\u20131167 (2017)","journal-title":"Vis. Comput."},{"issue":"4","key":"1781_CR17","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s00371-017-1354-0","volume":"34","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Lin, J., Li, W., Shi, Y., Cao, G.: Salient object detection via compactness and objectness cues. Vis. Comput. 34(4), 473\u2013489 (2018)","journal-title":"Vis. Comput."},{"key":"1781_CR18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1781_CR19","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Processing Systems, pp. 1106\u20131114 (2012)"},{"key":"1781_CR20","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3119\u20133127 (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"1781_CR21","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: STCT: sequentially training convolutional networks for visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1373\u20131381 (2016)","DOI":"10.1109\/CVPR.2016.153"},{"key":"1781_CR22","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H. Ruan, X., Yang, M.: Deep networks for saliency detection via local estimation and global search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3183\u20133192 (2015)","DOI":"10.1109\/CVPR.2015.7298938"},{"key":"1781_CR23","unstructured":"Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5455\u20135463 (2015)"},{"key":"1781_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265\u20131274 (2015)","DOI":"10.1109\/CVPR.2015.7298731"},{"key":"1781_CR25","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, L., Lu, H., Zhang, P., Ruan, X.: Saliency detection with recurrent fully convolutional networks. In: Proceedings of European Conference on Computer Vision, pp. 825\u2013841 (2016)","DOI":"10.1007\/978-3-319-46493-0_50"},{"key":"1781_CR26","unstructured":"Zhu, L., Hu, X., Fu, C.-W., Qin, J., Heng, P.-A.: Saliency-aware texture smoothing. In: IEEE Transactions on Visualization and Computer Graphics (2018)"},{"key":"1781_CR27","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, F., Cheng, H., Liu, W., Shen, D.: Contour knowledge transfer for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 355\u2013370 (2018)","DOI":"10.1007\/978-3-030-01267-0_22"},{"key":"1781_CR28","doi-asserted-by":"crossref","unstructured":"Chen, S., Tan, X., Wang, B., Hu, X.: Reverse attention for salient object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 234\u2013250 (2018)","DOI":"10.1007\/978-3-030-01240-3_15"},{"issue":"11","key":"1781_CR29","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1109\/TIP.2016.2601784","volume":"25","author":"W Wang","year":"2016","unstructured":"Wang, W., Shen, J., Shao, L., et al.: Correspondence driven saliency transfer. IEEE Trans. Image Process. 25(11), 5025\u20135034 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"1781_CR30","doi-asserted-by":"crossref","unstructured":"Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155\u20131162 (2013)","DOI":"10.1109\/CVPR.2013.153"},{"key":"1781_CR31","doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Ruan, X., Yang, M.-H.: Deep networks for saliency detection via local estimation and global search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3183\u20133192 (2015)","DOI":"10.1109\/CVPR.2015.7298938"},{"key":"1781_CR32","doi-asserted-by":"crossref","unstructured":"Li, G., Xie, Y., Lin, L., Yu, Y.: Instance-level salient object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2386\u20132395 (2017)","DOI":"10.1109\/CVPR.2017.34"},{"key":"1781_CR33","doi-asserted-by":"crossref","unstructured":"Chen, X., Zheng, A., Li, J., Lu, F.: Look, perceive and segment: finding the salient objects in images via two-stream fixation-semantic cnns. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1050\u20131058 (2017)","DOI":"10.1109\/ICCV.2017.119"},{"key":"1781_CR34","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. In: Proceedings of European Conference on Computer Vision, pp. 21\u201337 (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1781_CR35","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. CoRR, vol. arXiv:1612.03144 (2016)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1781_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, J., Lu, H., He, Y., Wang, G.: A bi-directional message passing model for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1741\u20131750 (2018)","DOI":"10.1109\/CVPR.2018.00187"},{"key":"1781_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, T., Qi, J., Lu, H., Wang, G.: Progressive attention guided recurrent network for salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 714\u2013722 (2018)","DOI":"10.1109\/CVPR.2018.00081"},{"key":"1781_CR38","doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083\u20132090 (2013)","DOI":"10.1109\/CVPR.2013.271"},{"issue":"5","key":"1781_CR39","first-page":"2368","volume":"27","author":"W W","year":"2017","unstructured":"W, W., J, S.: Deep visual attention prediction. IEEE Trans. Image Process. 27(5), 2368\u20132378 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"1781_CR40","first-page":"2014","volume":"23","author":"W W","year":"2016","unstructured":"W, W., J, S., Y, Y., et al.: Stereoscopic thumbnail creation via efficient stereo saliency detection. IEEE Trans. Vis. Comput. Graph. 23(8), 2014\u20132027 (2016)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"issue":"1","key":"1781_CR41","first-page":"38","volume":"27","author":"W W","year":"2017","unstructured":"W, W., J, S., L, S.: Video salient object detection via fully convolutional networks. IEEE Trans. Image Process. 27(1), 38\u201349 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"1781_CR42","doi-asserted-by":"crossref","unstructured":"Wang, W., Shen, J., Guo, F., et al.: Revisiting video saliency: a large-scale benchmark and a new model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4894\u20134903 (2018)","DOI":"10.1109\/CVPR.2018.00514"},{"issue":"1","key":"1781_CR43","first-page":"20","volume":"40","author":"W W","year":"2017","unstructured":"W, W., J, S., R, Y., et al.: Saliency-aware video object segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 40(1), 20\u201333 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"1781_CR44","doi-asserted-by":"crossref","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji, A., Cheng, M.-M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706\u20135722 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"1781_CR45","doi-asserted-by":"crossref","unstructured":"Wand, W., Shen, J., Dong, X., et al.: Salient object detection driven by fixation prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1711\u20131720 (2018)","DOI":"10.1109\/CVPR.2018.00184"},{"issue":"11","key":"1781_CR46","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","volume":"20","author":"L Itti","year":"1998","unstructured":"Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254\u20131259 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1781_CR47","unstructured":"Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23\u201328, 2013, pp. 3166\u20133173 (2013)"},{"key":"1781_CR48","doi-asserted-by":"crossref","unstructured":"Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.: Saliency detection via dense and sparse reconstruction. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2976\u20132983 (2013)","DOI":"10.1109\/ICCV.2013.370"},{"issue":"8","key":"1781_CR49","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1109\/TIP.2016.2579306","volume":"25","author":"X Li","year":"2016","unstructured":"Li, X., Zhao, L., Wei, L., Yang, M., Wu, F., Zhuang, Y., Ling, H., Wang, J.: Deepsaliency: multi-task deep neural network model for salient object detection. IEEE Trans. Image Process. 25(8), 3919\u20133930 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"1781_CR50","doi-asserted-by":"crossref","unstructured":"Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 478\u2013487 (2016)","DOI":"10.1109\/CVPR.2016.58"},{"key":"1781_CR51","doi-asserted-by":"crossref","unstructured":"Deng, Z., Hu, X., Zhu, L., Xu, X., Qin, J., Han, G., Heng, P.-A.: R3net: recurrent residual refinement network for saliency detection. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 684\u2013690. AAAI Press (2018)","DOI":"10.24963\/ijcai.2018\/95"},{"key":"1781_CR52","doi-asserted-by":"crossref","unstructured":"Liu, N., Han, J.: Dhsnet: deep hierarchical saliency network for salient object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 678\u2013686 (2016)","DOI":"10.1109\/CVPR.2016.80"},{"key":"1781_CR53","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.-M., Hu, X., Borji, A., Tu, Z., Torr, P.H.: Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3203\u20133212 (2017)","DOI":"10.1109\/CVPR.2017.563"},{"key":"1781_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 202\u2013211 (2017)","DOI":"10.1109\/ICCV.2017.31"},{"key":"1781_CR55","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhu, L., Qin, J., Fu, C.-W., Heng, P.-A.: Recurrently aggregating deep features for salient object detection. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12298"},{"key":"1781_CR56","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., et al.: A simple pooling-based design for real-time salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"1781_CR57","unstructured":"Qin, X., Zhang, Z., Huang, C., et al.: Basnet: boundary-aware salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"1781_CR58","doi-asserted-by":"crossref","unstructured":"Hu, X., Fu, C.W., Zhu, L., et al.: Sac-net: spatial attenuation context for salient object detection. arXiv preprint arXiv:1903.10152 (2019)","DOI":"10.1109\/TCSVT.2020.2995220"},{"key":"1781_CR59","unstructured":"Wu, Z., Su, L., Huang, Q.: Cascaded partial decoder for fast and accurate salient object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"1781_CR60","unstructured":"Borji, A., Sihite, D.N., Itti, L.: Salient object detection: a benchmark. In: Computer Vision\u2014ECCV 2012\u201412th European Conference on Computer Vision, Florence, Italy, October 7\u201313, 2012, Proceedings, Part II, pp. 414\u2013429 (2012)"},{"key":"1781_CR61","doi-asserted-by":"crossref","unstructured":"Mai, L., Niu, Y., Liu, F.: Saliency aggregation: a data-driven approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1131\u20131138 (2013)","DOI":"10.1109\/CVPR.2013.150"},{"issue":"144","key":"1781_CR62","first-page":"491","volume":"32","author":"AN Tikhonov","year":"1977","unstructured":"Tikhonov, A.N., Arsenin, V.Y.: Solution of ill-posed problems. Math. Comput. 32(144), 491\u2013491 (1977)","journal-title":"Math. Comput."},{"key":"1781_CR63","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, vol. arXiv:1409.1556 (2014)"},{"key":"1781_CR64","unstructured":"Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A.C., Bengio, Y.: Maxout networks. In: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16\u201321 June 2013, pp. 1319\u20131327 (2013)"},{"key":"1781_CR65","volume-title":"Digital Image Processing Using MATLAB","author":"RC Gonz\u00e1lez","year":"2004","unstructured":"Gonz\u00e1lez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson-Prentice-Hall, Upper Saddle River (2004)"},{"key":"1781_CR66","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 280\u2013287 (2014)","DOI":"10.1109\/CVPR.2014.43"},{"key":"1781_CR67","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S.S., Estrada, F.J., S\u00fcsstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597\u20131604 (2009)","DOI":"10.1109\/CVPR.2009.5206596"},{"issue":"3","key":"1781_CR68","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","volume":"37","author":"M Cheng","year":"2015","unstructured":"Cheng, M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569\u2013582 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1781_CR69","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. CoRR, vol. arXiv:1408.5093 (2014)","DOI":"10.1145\/2647868.2654889"},{"issue":"4","key":"1781_CR70","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TPAMI.2016.2562626","volume":"39","author":"H Peng","year":"2017","unstructured":"Peng, H., Li, B., Ling, H., Hu, W., Xiong, W., Maybank, S.J.: Salient object detection via structured matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 818\u2013832 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1781_CR71","doi-asserted-by":"crossref","unstructured":"Tong, N., Lu, H., Ruan, X., Yang, M.: Salient object detection via bootstrap learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1884\u20131892 (2015)","DOI":"10.1109\/CVPR.2015.7298798"},{"key":"1781_CR72","doi-asserted-by":"crossref","unstructured":"Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.: Saliency detection via absorbing Markov chain. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1665\u20131672 (2013)","DOI":"10.1109\/ICCV.2013.209"},{"key":"1781_CR73","doi-asserted-by":"crossref","unstructured":"Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2814\u20132821 (2014)","DOI":"10.1109\/CVPR.2014.360"},{"key":"1781_CR74","doi-asserted-by":"crossref","unstructured":"Zhang, J., Sclaroff, S., Lin, Z.L., Shen, X., Price, B.L., Mech, R.: Minimum barrier salient object detection at 80 FPS. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1404\u20131412 (2015)","DOI":"10.1109\/ICCV.2015.165"},{"key":"1781_CR75","unstructured":"Qin, Y., Lu, H., Xu, Y., Wang, H.: Saliency detection via cellular automata. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 110\u2013119 (2015)"},{"key":"1781_CR76","doi-asserted-by":"crossref","unstructured":"Lee, G., Tai, Y., Kim, J.: Deep saliency with encoded low level distance map and high level features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 660\u2013668 (2016)","DOI":"10.1109\/CVPR.2016.78"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-019-01781-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00371-019-01781-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-019-01781-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:54:55Z","timestamp":1665190495000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00371-019-01781-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,9]]},"references-count":76,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["1781"],"URL":"https:\/\/doi.org\/10.1007\/s00371-019-01781-9","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2019,12,9]]},"assertion":[{"value":"9 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}