{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T20:31:24Z","timestamp":1754598684954},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"41-42","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["cstc2017jcyjAX0142"],"award-info":[{"award-number":["cstc2017jcyjAX0142"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Commission","doi-asserted-by":"publisher","award":["cstc2018jcyjAX0525"],"award-info":[{"award-number":["cstc2018jcyjAX0525"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Department of Science and Technology of Sichuan Province","doi-asserted-by":"publisher","award":["2019YFG0107"],"award-info":[{"award-number":["2019YFG0107"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1007\/s11042-020-09556-4","type":"journal-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T08:03:42Z","timestamp":1597910622000},"page":"31283-31298","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hierarchical saliency mapping for weakly supervised object localization based on class activation mapping"],"prefix":"10.1007","volume":"79","author":[{"given":"Zhuo","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Hongjian","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiangyan","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Meiqi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Duan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"9556_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10674-4","volume-title":"Feature selection and enhanced krill herd algorithm for text document clustering","author":"LMQ Abualigah","year":"2019","unstructured":"Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin"},{"key":"9556_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.5121\/ijcsea.2015.5102","volume":"5","author":"L Abualigah","year":"2015","unstructured":"Abualigah L, Hanandeh E (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19\u201328. https:\/\/doi.org\/10.5121\/ijcsea.2015.5102","journal-title":"Int J Comput Sci Eng Appl"},{"issue":"11","key":"9556_CR3","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1007\/s11227-017-2046-2","volume":"73","author":"LM Abualigah","year":"2017","unstructured":"Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773\u20134795","journal-title":"J Supercomput"},{"key":"9556_CR4","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.asoc.2017.06.059","volume":"60","author":"LM Abualigah","year":"2017","unstructured":"Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423\u2013435","journal-title":"Appl Soft Comput"},{"key":"9556_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.engappai.2018.05.003","volume":"73","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111\u2013125. https:\/\/doi.org\/10.1016\/j.engappai.2018.05.003","journal-title":"Eng Appl Artif Intell"},{"issue":"11","key":"9556_CR6","doi-asserted-by":"publisher","first-page":"4047","DOI":"10.1007\/s10489-018-1190-6","volume":"48","author":"LM Abualigah","year":"2018","unstructured":"Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047\u20134071","journal-title":"Appl Intell"},{"key":"9556_CR7","doi-asserted-by":"crossref","unstructured":"Bazzani L, Bergamo A, Anguelov D, Torresani L (2016) Self-taught object localization with deep networks. In: 2016 IEEE Winter conference on applications of computer vision (WACV). IEEE, pp 1\u20139","DOI":"10.1109\/WACV.2016.7477688"},{"key":"9556_CR8","doi-asserted-by":"publisher","unstructured":"Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2018.2815149","DOI":"10.1109\/ACCESS.2018.2815149"},{"issue":"1","key":"9556_CR9","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TPAMI.2016.2535231","volume":"39","author":"RG Cinbis","year":"2016","unstructured":"Cinbis RG, Verbeek J, Schmid C (2016) Weakly supervised object localization with multi-fold multiple instance learning. IEEE Trans Pattern Anal Mach Intell 39(1):189\u2013203","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9556_CR10","doi-asserted-by":"crossref","unstructured":"Fan DP, Liu JJ, Gao S, Hou Q, Borji A, Cheng MM (2018) Salient objects in clutter: bringing salient object detection to the foreground. In: European conference on computer vision (ECCV), pp 196\u2013212","DOI":"10.1007\/978-3-030-01267-0_12"},{"key":"9556_CR11","unstructured":"Fan DP, Lin Z, Zhao JX, Liu Y, Zhang Z, Hou Q, Zhu M, Cheng MM (2019) Rethinking rgb-d salient object detection: models, datasets and large-scale benchmarks"},{"key":"9556_CR12","doi-asserted-by":"crossref","unstructured":"Fan DP, Wang W, Cheng MM, Shen J (2019) Shifting more attention to video salient object detection. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 8554\u20138564","DOI":"10.1109\/CVPR.2019.00875"},{"issue":"1","key":"9556_CR13","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cviu.2005.09.012","volume":"106","author":"L Fei-Fei","year":"2007","unstructured":"Fei-Fei L, Fergus R, Perona P (2007) Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis Image Underst 106(1):59\u201370","journal-title":"Comput Vis Image Underst"},{"key":"9556_CR14","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2019.04.062","volume":"356","author":"K Fu","year":"2019","unstructured":"Fu K, Zhao Q, Gu IY, Yang J (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69\u201382","journal-title":"Neurocomputing"},{"key":"9556_CR15","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"9556_CR16","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"issue":"9","key":"9556_CR17","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9556_CR18","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580"},{"key":"9556_CR19","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"9556_CR20","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"6","key":"9556_CR21","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1016\/j.jnca.2011.07.003","volume":"34","author":"L Leng","year":"2011","unstructured":"Leng L, Zhang J (2011) Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security. J Netw Comput Appl 34(6):1979\u20131989. https:\/\/doi.org\/10.1016\/j.jnca.2011.07.003. Control and Optimization over Wireless Networks","journal-title":"J Netw Comput Appl"},{"key":"9556_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2012.08.028","volume":"108","author":"L Leng","year":"2013","unstructured":"Leng L, Zhang J (2013) Palmhash code vs. palmphasor code. Neurocomputing 108:1\u201312. https:\/\/doi.org\/10.1016\/j.neucom.2012.08.028","journal-title":"Neurocomputing"},{"key":"9556_CR23","doi-asserted-by":"crossref","unstructured":"Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in dct domain for face and palmprint recognition. In: 2010 international conference on information and communication technology convergence (ICTC). IEEE, pp 467\u2013471","DOI":"10.1109\/ICTC.2010.5674791"},{"key":"9556_CR24","doi-asserted-by":"crossref","unstructured":"Leng L, Li M, Teoh ABJ (2013) Conjugate 2dpalmhash code for secure palm-print-vein verification. In: 2013 6th International congress on image and signal processing (CISP), vol 3. IEEE, pp 1705\u20131710","DOI":"10.1109\/CISP.2013.6743951"},{"issue":"1","key":"9556_CR25","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s11042-015-3058-7","volume":"76","author":"L Leng","year":"2017","unstructured":"Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76(1):333\u2013354","journal-title":"Multimed Tools Appl"},{"key":"9556_CR26","doi-asserted-by":"crossref","unstructured":"Li D, Huang JB, Li Y, Wang S, Yang MH (2019) Progressive representation adaptation for weakly supervised object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2019.2899839"},{"issue":"2","key":"9556_CR27","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1007\/s11071-019-05170-8","volume":"98","author":"P Liu","year":"2019","unstructured":"Liu P, Yu H, Cang S (2019) Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dyn 98(2):1447\u20131464","journal-title":"Nonlinear Dyn"},{"key":"9556_CR28","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9556_CR29","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1717\u20131724","DOI":"10.1109\/CVPR.2014.222"},{"key":"9556_CR30","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (2015) Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 685\u2013694","DOI":"10.1109\/CVPR.2015.7298668"},{"issue":"4","key":"9556_CR31","first-page":"411","volume":"9","author":"D Kumar","year":"2017","unstructured":"Preeti, Kumar D (2017) Feature selection for face recognition using dct-pca and bat algorithm. Int J Inf Technol 9(4):411\u2013423","journal-title":"Int J Inf Technol"},{"key":"9556_CR32","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91\u201399"},{"issue":"3","key":"9556_CR33","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"9556_CR34","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"9556_CR35","doi-asserted-by":"crossref","unstructured":"Song H, Wang W, Zhao S, Shen J, Lam KM (2018) Pyramid dilated deeper convlstm for video salient object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 715\u2013731","DOI":"10.1007\/978-3-030-01252-6_44"},{"issue":"9","key":"9556_CR36","doi-asserted-by":"publisher","first-page":"3487","DOI":"10.1109\/JSEN.2018.2888815","volume":"19","author":"L Sun","year":"2019","unstructured":"Sun L, Zhao C, Yan Z, Liu P, Duckett T, Stolkin R (2019) A novel weakly-supervised approach for rgb-d-based nuclear waste object detection. IEEE Sens J 19(9):3487\u20133500","journal-title":"IEEE Sens J"},{"key":"9556_CR37","unstructured":"Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. In: International conference on machine learning, pp 1139\u20131147"},{"issue":"9","key":"9556_CR38","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1109\/TMM.2017.2729786","volume":"19","author":"S Tang","year":"2017","unstructured":"Tang S, Li Y, Deng L, Zhang Y (2017) Object localization based on proposal fusion. IEEE Trans Multimed 19(9):2105\u20132116","journal-title":"IEEE Trans Multimed"},{"key":"9556_CR39","unstructured":"Wah C, Branson S, Welinder P, Perona P, Belongie S (2011) The caltech-ucsd birds-200-2011 dataset. California Institute of Technology"},{"key":"9556_CR40","doi-asserted-by":"crossref","unstructured":"Wan Z, He H (2017) Weakly supervised object localization with deep convolutional neural network based on spatial pyramid saliency map. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 4177\u20134181","DOI":"10.1109\/ICIP.2017.8297069"},{"key":"9556_CR41","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.3390\/electronics8101072","volume":"8","author":"S Xia","year":"2019","unstructured":"Xia S, Zeng J, Leng L, Fu X (2019) Ws-am: weakly supervised attention map for scene recognition. Electronics 8:1072. https:\/\/doi.org\/10.3390\/electronics8101072","journal-title":"Electronics"},{"key":"9556_CR42","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"9556_CR43","doi-asserted-by":"crossref","unstructured":"Zhang X, Wei Y, Feng J, Yang Y, Huang TS (2018) Adversarial complementary learning for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1325\u20131334","DOI":"10.1109\/CVPR.2018.00144"},{"key":"9556_CR44","doi-asserted-by":"crossref","unstructured":"Zhao J, Cao Y, Fan D, Cheng M, Li X, Zhang L (2019) Contrast prior and fluid pyramid integration for rgbd salient object detection. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3922\u20133931","DOI":"10.1109\/CVPR.2019.00405"},{"key":"9556_CR45","doi-asserted-by":"crossref","unstructured":"Zhao JX, Liu JJ, Fan DP, Cao Y, Yang J, Cheng MM (2019) Egnet: edge guidance network for salient object detection. In: Proceedings of the IEEE international conference on computer vision, pp 8779\u20138788","DOI":"10.1109\/ICCV.2019.00887"},{"key":"9556_CR46","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2014) Object detectors emerge in deep scene cnns. arXiv:1412.6856"},{"key":"9556_CR47","unstructured":"Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Advances in neural information processing systems, pp 487\u2013495"},{"key":"9556_CR48","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09556-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09556-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09556-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T00:03:47Z","timestamp":1629417827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09556-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":48,"journal-issue":{"issue":"41-42","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["9556"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09556-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"18 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}