{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:03:52Z","timestamp":1767261832571,"version":"3.37.3"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T00:00:00Z","timestamp":1602115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T00:00:00Z","timestamp":1602115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11042-020-09809-2","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T21:02:32Z","timestamp":1602190952000},"page":"5809-5831","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Color object segmentation and tracking using flexible statistical model and level-set"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6638-7039","authenticated-orcid":false,"given":"Sami","family":"Bourouis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ines","family":"Channoufi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1585-2962","authenticated-orcid":false,"given":"Roobaea","family":"Alroobaea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4433-5529","authenticated-orcid":false,"given":"Saeed","family":"Rubaiee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murad","family":"Andejany","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7224-7940","authenticated-orcid":false,"given":"Nizar","family":"Bouguila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,8]]},"reference":[{"key":"9809_CR1","doi-asserted-by":"publisher","first-page":"52181","DOI":"10.1109\/ACCESS.2019.2912115","volume":"7","author":"W Alhakami","year":"2019","unstructured":"Alhakami W, ALharbi A, Bourouis S, Alroobaea R, Bouguila N (2019) Network anomaly intrusion detection using a nonparametric bayesian approach and feature selection. IEEE Access 7:52181\u201352190","journal-title":"IEEE Access"},{"key":"9809_CR2","doi-asserted-by":"crossref","unstructured":"Allili MS, Ziou D, Bouguila N, Boutemedjet S (2010) Unsupervised feature selection and learning for image segmentation. In: 2010 Canadian conference on computer and robot vision (CRV). IEEE, pp 285\u2013292","DOI":"10.1109\/CRV.2010.44"},{"issue":"9","key":"9809_CR3","first-page":"6795","volume":"13","author":"R Alroobaea","year":"2018","unstructured":"Alroobaea R, Alsufyani A, Ansari MA, Rubaiee S, Algarni S (2018) Supervised machine learning of kfcg algorithm and mbtc features for efficient classification of image database and cbir systems. Int J Appl Eng Res 13(9):6795\u20136804","journal-title":"Int J Appl Eng Res"},{"issue":"1","key":"9809_CR4","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1002\/ima.22391","volume":"30","author":"R Alroobaea","year":"2020","unstructured":"Alroobaea R, Rubaiee S, Bourouis S, Bouguila N, Alsufyani A (2020) Bayesian inference framework for bounded generalized gaussian-based mixture model and its application to biomedical images classification. Int J Imaging Syst Technol 30(1):18\u201330","journal-title":"Int J Imaging Syst Technol"},{"key":"9809_CR5","doi-asserted-by":"crossref","unstructured":"Arbelaez P (2006) Boundary extraction in natural images using ultrametric contour maps. In: IEEE conference on computer vision and pattern recognition, CVPR, p 182","DOI":"10.1109\/CVPRW.2006.48"},{"issue":"5","key":"9809_CR6","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2011","unstructured":"Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898\u2013916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"9809_CR7","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1109\/TPAMI.2005.106","volume":"27","author":"IB Ayed","year":"2005","unstructured":"Ayed IB, Mitiche A, Belhadj Z (2005) Multiregion level-set partitioning of synthetic aperture radar images. IEEE Trans Pattern Anal Mach Intell 27 (5):793\u2013800","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9809_CR8","doi-asserted-by":"crossref","unstructured":"Babu G, Aneesh R, Nayar GR (2017) A novel method based on chan vese segmentation for salient structure detection. In: 2017 IEEE international conference on circuits and systems (ICCS). IEEE, pp 414\u2013418","DOI":"10.1109\/ICCS1.2017.8326033"},{"key":"9809_CR9","doi-asserted-by":"crossref","unstructured":"Bouguila N, Ziou D (2005) On fitting finite dirichlet mixture using ecm and mml. In: Wang P, Singh M, Apt\u00e9 C, Perner P (eds) Pattern recognition and data mining, third international conference on advances in pattern recognition, ICAPR 2005, Bath, UK, August 22\u201325, 2005, proceedings, Part I, vol 3686. Springer, pp 172\u2013182","DOI":"10.1007\/11551188_19"},{"issue":"2","key":"9809_CR10","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11222-006-8451-7","volume":"16","author":"N Bouguila","year":"2006","unstructured":"Bouguila N, Ziou D, Monga E (2006) Practical bayesian estimation of a finite beta mixture through gibbs sampling and its applications. Stat Comput 16(2):215\u2013225","journal-title":"Stat Comput"},{"issue":"1","key":"9809_CR11","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1142\/S0219467810003706","volume":"10","author":"S Bourouis","year":"2010","unstructured":"Bourouis S, Hamrouni K (2010) 3d segmentation of MRI brain using level set and unsupervised classification. Int J Image Graph 10(1):135\u2013154","journal-title":"Int J Image Graph"},{"key":"9809_CR12","doi-asserted-by":"crossref","unstructured":"Bourouis S, Hamrouni K, Betrouni N (2008) Automatic MRI brain segmentation with combined atlas-based classification and level-set approach. In: 5th International conference, ICIAR 2008 image analysis and recognition, P\u00f3voa de Varzim, Portugal, June 25\u201327, 2008. Proceedings, pp 770\u2013778","DOI":"10.1007\/978-3-540-69812-8_76"},{"issue":"5","key":"9809_CR13","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1016\/j.eswa.2013.09.030","volume":"41","author":"S Bourouis","year":"2014","unstructured":"Bourouis S, Al Mashrgy M, Bouguila N (2014) Bayesian learning of finite generalized inverted dirichlet mixtures: application to object classification and forgery detection. Exp Syst Appl 41(5):2329\u20132336","journal-title":"Exp Syst Appl"},{"key":"9809_CR14","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1109\/ACCESS.2018.2886315","volume":"7","author":"S Bourouis","year":"2019","unstructured":"Bourouis S, Zaguia A, Bouguila N, Alroobaea R (2019) Deriving probabilistic SVM kernels from flexible statistical mixture models and its application to retinal images classification. IEEE Access 7:1107\u20131117","journal-title":"IEEE Access"},{"key":"9809_CR15","doi-asserted-by":"crossref","unstructured":"Boutemedjet S, Bouguila N, Ziou D (2007) Feature selection for non gaussian mixture models. In: 2007 IEEE workshop on machine learning for signal processing. IEEE, pp 69\u201374","DOI":"10.1109\/MLSP.2007.4414284"},{"issue":"2","key":"9809_CR16","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"TF Chan","year":"2001","unstructured":"Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266\u2013277","journal-title":"IEEE Trans Image Process"},{"key":"9809_CR17","doi-asserted-by":"crossref","unstructured":"Channoufi I, Bourouis S, Bouguila N, Hamrouni K (2018) Color image segmentation with bounded generalized gaussian mixture model and feature selection. In: 4th International conference on advanced technologies for signal and image processing, ATSIP 2018, Sousse, Tunisia, March 21\u201324, 2018, pp 1\u20136","DOI":"10.1109\/ATSIP.2018.8364459"},{"issue":"19","key":"9809_CR18","doi-asserted-by":"publisher","first-page":"25591","DOI":"10.1007\/s11042-018-5808-9","volume":"77","author":"I Channoufi","year":"2018","unstructured":"Channoufi I, Bourouis S, Bouguila N, Hamrouni K (2018) Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information. Multimed Tools Appl 77(19):25591\u201325606","journal-title":"Multimed Tools Appl"},{"key":"9809_CR19","first-page":"325","volume-title":"Flexible statistical learning model for unsupervised image modeling and segmentation","author":"I Channoufi","year":"2020","unstructured":"Channoufi I, Najar F, Bourouis S, Azam M, Halibas AS, Alroobaea R, Al-Badi A (2020) Flexible statistical learning model for unsupervised image modeling and segmentation. Springer International Publishing, Berlin, pp 325\u2013348"},{"issue":"3","key":"9809_CR20","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.patcog.2014.09.010","volume":"48","author":"Y Cong","year":"2015","unstructured":"Cong Y, Wang S, Liu J, Cao J, Yang Y, Luo J (2015) Deep sparse feature selection for computer aided endoscopy diagnosis. Pattern Recognit 48(3):907\u2013917","journal-title":"Pattern Recognit"},{"key":"9809_CR21","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cviu.2016.08.005","volume":"152","author":"C Cuevas","year":"2016","unstructured":"Cuevas C, Y\u00e1\u00f1ez EM, Garc\u00eda N (2016) Labeled dataset for integral evaluation of moving object detection algorithms: Lasiesta. Comput Vis Image Underst 152:103\u2013117","journal-title":"Comput Vis Image Underst"},{"issue":"12","key":"9809_CR22","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1109\/TIP.2008.2006443","volume":"17","author":"C Darolti","year":"2008","unstructured":"Darolti C, Mertins A, Bodensteiner C, Hofmann UG (2008) Local region descriptors for active contours evolution. IEEE Trans Image Process 17 (12):2275\u20132288","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"9809_CR23","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1109\/TMI.2009.2038693","volume":"29","author":"O Dzyubachyk","year":"2010","unstructured":"Dzyubachyk O, Van Cappellen WA, Essers J, Niessen WJ, Meijering E (2010) Advanced level-set-based cell tracking in time-lapse fluorescence microscopy. IEEE Trans Med Imaging 29(3):852\u2013867","journal-title":"IEEE Trans Med Imaging"},{"key":"9809_CR24","doi-asserted-by":"crossref","unstructured":"Falco ID, Pietro GD, Cioppa AD, Sannino G, Scafuri U, Tarantino E (2018) Preliminary steps towards efficient classification in large medical datasets: structure optimization for deep learning networks through parallelized differential evolution. In: 11th International joint conference on biomedical engineering systems and technologies (BIOSTEC), pp 633\u2013640","DOI":"10.5220\/0006730006330640"},{"issue":"3","key":"9809_CR25","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/34.990138","volume":"24","author":"MAT Figueiredo","year":"2002","unstructured":"Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381\u2013396","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9809_CR26","doi-asserted-by":"crossref","unstructured":"Freixenet J, Mu\u00f1oz X, Raba D, Mart\u00ed J, Cuf\u00ed X (2002) Yet another survey on image segmentation: region and boundary information integration. In: Heyden A, Sparr G, Nielsen M, Johansen P (eds) Computer vision - ECCV 2002, 7th European conference on computer vision, copenhagen, Denmark, May 28-31, 2002, proceedings, Part III, vol 2352. Springer, pp 408\u2013422","DOI":"10.1007\/3-540-47977-5_27"},{"key":"9809_CR27","doi-asserted-by":"crossref","unstructured":"Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1. Springer series in statistics. New York","DOI":"10.1007\/978-0-387-21606-5_1"},{"key":"9809_CR28","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.future.2018.12.039","volume":"94","author":"Z Gao","year":"2019","unstructured":"Gao Z, Wang D, Wan S, Zhang H, Wang Y (2019) Cognitive-inspired class-statistic matching with triple-constrain for camera free 3d object retrieval. Future Gener Comput Syst 94:641\u2013653","journal-title":"Future Gener Comput Syst"},{"key":"9809_CR29","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.neunet.2020.02.017","volume":"125","author":"Z Gao","year":"2020","unstructured":"Gao Z, Xue H, Wan S (2020) Multiple discrimination and pairwise CNN for view-based 3d object retrieval. Neural Netw 125:290\u2013302","journal-title":"Neural Netw"},{"key":"9809_CR30","doi-asserted-by":"crossref","unstructured":"Girshick RB (2015) Fast R-CNN. In: 2015 IEEE international conference on computer vision, ICCV 2015, Santiago, Chile, December 7\u201313, 2015, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"9809_CR31","doi-asserted-by":"crossref","unstructured":"Girshick RB, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"9809_CR32","doi-asserted-by":"crossref","unstructured":"Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. In: 1997 IEEE computer society conference on computer vision and pattern recognition, 1997. Proceedings. IEEE, pp 762\u2013768","DOI":"10.1109\/CVPR.1997.609412"},{"key":"9809_CR33","doi-asserted-by":"crossref","unstructured":"Ilyasova N, Paringer R, Kupriyanov A, Kirsh D (2017) Intelligent feature selection technique for segmentation of fundus images. In: 2017 Seventh international conference on innovative computing technology (INTECH), pp 138\u2013143","DOI":"10.1109\/INTECH.2017.8102433"},{"issue":"2","key":"9809_CR34","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s10044-015-0502-2","volume":"20","author":"K Jackowski","year":"2017","unstructured":"Jackowski K, Cyganek B (2017) A learning-based colour image segmentation with extended and compact structural tensor feature representation. Pattern Anal Appl 20(2):401\u2013414","journal-title":"Pattern Anal Appl"},{"key":"9809_CR35","doi-asserted-by":"crossref","unstructured":"Junfeng L, Jinwen M (2016) Effective selection of mixed color features for image segmentation. In: 2016 IEEE 13th international conference on signal processing (ICSP), pp 794\u2013798","DOI":"10.1109\/ICSP.2016.7877940"},{"issue":"9","key":"9809_CR36","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1109\/TPAMI.2004.71","volume":"26","author":"MH Law","year":"2004","unstructured":"Law MH, Figueiredo MA, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26 (9):1154\u20131166","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9809_CR37","doi-asserted-by":"crossref","unstructured":"Li Y, Guo L (2008) TCM-KNN scheme for network anomaly detection using feature-based optimizations. In: Proceedings of the 2008 ACM symposium on applied computing (SAC), Fortaleza, Ceara, Brazil, March 16\u201320, 2008, pp 2103\u20132109","DOI":"10.1145\/1363686.1364194"},{"issue":"12","key":"9809_CR38","doi-asserted-by":"publisher","first-page":"5343","DOI":"10.1109\/TIP.2015.2479560","volume":"24","author":"Z Li","year":"2015","unstructured":"Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343\u20135355","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"9809_CR39","doi-asserted-by":"publisher","first-page":"2138","DOI":"10.1109\/TKDE.2013.65","volume":"26","author":"Z Li","year":"2014","unstructured":"Li Z, Liu J, Yang Y, Zhou X, Lu H (2014) Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Trans Knowl Data Eng 26(9):2138\u20132150","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9809_CR40","doi-asserted-by":"crossref","unstructured":"Lin T, Maire M, Belongie SJ, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: 13th European conference ECCV, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"9809_CR41","doi-asserted-by":"crossref","unstructured":"Lin T, Doll\u00e1r P, Girshick RB, He K, Hariharan B, Belongie SJ (2017) Feature pyramid networks for object detection. In: IEEE Conference on computer vision and pattern recognition, CVPR, pp 936\u2013944","DOI":"10.1109\/CVPR.2017.106"},{"issue":"1","key":"9809_CR42","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/TSA.2002.805639","volume":"11","author":"J Lindblom","year":"2003","unstructured":"Lindblom J, Samuelsson J (2003) Bounded support gaussian mixture modeling of speech spectra. IEEE Trans Speech Audio Process 11(1):88\u201399","journal-title":"IEEE Trans Speech Audio Process"},{"key":"9809_CR43","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed SE, Fu C, Berg AC (2016) SSD: single shot multibox detector. In: Computer vision\u2014ECCV 2016\u201414th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"9809_CR44","doi-asserted-by":"crossref","unstructured":"Maire M, Arbelaez P, Fowlkes CC, Malik J (2008) Using contours to detect and localize junctions in natural images. In: 2008 IEEE Computer society conference on computer vision and pattern recognition CVPR","DOI":"10.1109\/CVPR.2008.4587420"},{"key":"9809_CR45","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Eighth IEEE international conference on computer vision, 2001. ICCV 2001. Proceedings, vol 2. IEEE, pp 416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"9809_CR46","doi-asserted-by":"crossref","unstructured":"Ma\u0161ka M, Matula P, Dan\u011bk O, Kozubek M (2010) A fast level set-like algorithm for region-based active contours. In: International symposium on visual computing. Springer, pp 387\u2013396","DOI":"10.1007\/978-3-642-17277-9_40"},{"key":"9809_CR47","doi-asserted-by":"publisher","DOI":"10.1002\/0471721182","volume-title":"Finite mixture models","author":"G McLachlan","year":"2000","unstructured":"McLachlan G, Peel D (2000) Finite mixture models. Wiley, New York"},{"issue":"6","key":"9809_CR48","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1109\/TIP.2006.873455","volume":"15","author":"S Meignen","year":"2006","unstructured":"Meignen S, Meignen H (2006) On the modeling of small sample distributions with generalized gaussian density in a maximum likelihood framework. IEEE Trans Image Process 15(6):1647\u20131652","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"9809_CR49","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1109\/TIP.2010.2044965","volume":"19","author":"M Mignotte","year":"2010","unstructured":"Mignotte M (2010) A label field fusion bayesian model and its penalized maximum rand estimator for image segmentation. IEEE Trans Image Process 19 (6):1610\u20131624","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"9809_CR50","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1002\/cpa.3160420503","volume":"42","author":"D Mumford","year":"1989","unstructured":"Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577\u2013685","journal-title":"Commun Pure Appl Math"},{"key":"9809_CR51","doi-asserted-by":"crossref","unstructured":"Najar F, Bourouis S, Bouguila N, Belghith S (2017) A comparison between different gaussian-based mixture models. In: 14th IEEE\/ACS International conference on computer systems and applications, AICCSA 2017, Hammamet, Tunisia, October 30\u2013Nov. 3, 2017, pp 704\u2013708","DOI":"10.1109\/AICCSA.2017.108"},{"key":"9809_CR52","doi-asserted-by":"crossref","unstructured":"Najar F, Bourouis S, Zaguia A, Bouguila N, Belghith S (2018) Unsupervised human action categorization using a riemannian averaged fixed-point learning of multivariate GGMM. In: Image analysis and recognition - 15th international conference, ICIAR, pp 408\u2013415","DOI":"10.1007\/978-3-319-93000-8_46"},{"issue":"13","key":"9809_CR53","doi-asserted-by":"publisher","first-page":"18669","DOI":"10.1007\/s11042-018-7116-9","volume":"78","author":"F Najar","year":"2019","unstructured":"Najar F, Bourouis S, Bouguila N, Belghith S (2019) Unsupervised learning of finite full covariance multivariate generalized gaussian mixture models for human activity recognition. Multimed Tools Appl 78(13):18669\u201318691","journal-title":"Multimed Tools Appl"},{"issue":"14","key":"9809_CR54","doi-asserted-by":"publisher","first-page":"10611","DOI":"10.1007\/s00500-019-04567-2","volume":"24","author":"F Najar","year":"2020","unstructured":"Najar F, Bourouis S, Bouguila N, Belghith S (2020) A new hybrid discriminative\/generative model using the full-covariance multivariate generalized gaussian mixture models. Soft Comput 24(14):10611\u201310628","journal-title":"Soft Comput"},{"key":"9809_CR55","doi-asserted-by":"crossref","unstructured":"Oussalah M, Shabash M (2012) Object tracking using level set and mpeg 7 color features. In: 2012 3rd International conference on image processing theory, tools and applications (IPTA). IEEE, pp 105\u2013110","DOI":"10.1109\/IPTA.2012.6469575"},{"issue":"14","key":"9809_CR56","doi-asserted-by":"publisher","first-page":"1710","DOI":"10.1016\/j.patrec.2006.04.019","volume":"27","author":"M Pi","year":"2006","unstructured":"Pi M (2006) Improve maximum likelihood estimation for subband ggd parameters. Pattern Recognit Lett 27(14):1710\u20131713","journal-title":"Pattern Recognit Lett"},{"key":"9809_CR57","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, USA, July 21\u201326, 2017, pp 6517\u20136525","DOI":"10.1109\/CVPR.2017.690"},{"key":"9809_CR58","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala SK, Girshick RB, Farhadi A (2016) You only look once: unified, real-time object detection. In: IEEE Conference on computer vision and pattern recognition, CVPR, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"9809_CR59","volume-title":"Level set methods and fast marching methods: evolving interfaces in geometry, fluid mechanics, computer vision, and materials science","author":"J Sethian","year":"1999","unstructured":"Sethian J (1999) Level set methods and fast marching methods: evolving interfaces in geometry, fluid mechanics, computer vision, and materials science, 2nd edn. Cambridge University Press, Cambridge","edition":"2nd edn."},{"issue":"1","key":"9809_CR60","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.cmpb.2012.09.004","volume":"113","author":"P Szczypi\u0144ski","year":"2014","unstructured":"Szczypi\u0144ski P, Klepaczko A, Pazurek M, Daniel P (2014) Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Computer Methods Progr Biomed 113(1):396\u2013411","journal-title":"Computer Methods Progr Biomed"},{"key":"9809_CR61","doi-asserted-by":"crossref","unstructured":"Tychsen-Smith L, Petersson L (2017) Denet: scalable real-time object detection with directed sparse sampling. In: 2017 IEEE International conference on computer vision (ICCV). IEEE, pp 428\u2013436","DOI":"10.1109\/ICCV.2017.54"},{"key":"9809_CR62","unstructured":"Wallace CS (2005) Statistical and inductive inference by minimum message length. Springer Science & Business Media"},{"issue":"2","key":"9809_CR63","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s11263-016-0977-3","volume":"123","author":"J Wang","year":"2017","unstructured":"Wang J, Jiang H, Yuan Z, Cheng M, Hu X, Zheng N (2017) Salient object detection: a discriminative regional feature integration approach. Int J Comput Vis 123(2):251\u2013268","journal-title":"Int J Comput Vis"},{"issue":"4","key":"9809_CR64","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.imavis.2009.10.009","volume":"28","author":"K Zhang","year":"2010","unstructured":"Zhang K, Zhang L, Song H, Zhou W (2010) Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis Comput 28(4):668\u2013676","journal-title":"Image Vis Comput"},{"issue":"12","key":"9809_CR65","doi-asserted-by":"publisher","first-page":"4664","DOI":"10.1109\/TIP.2013.2277800","volume":"22","author":"K Zhang","year":"2013","unstructured":"Zhang K, Zhang L, Yang MH (2013) Real-time object tracking via online discriminative feature selection. IEEE Trans Image Process 22 (12):4664\u20134677","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"9809_CR66","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1109\/TCSVT.2013.2269772","volume":"23","author":"K Zhang","year":"2013","unstructured":"Zhang K, Zhang L, Yang MH, Hu Q (2013) Robust object tracking via active feature selection. IEEE Trans Circ Syst Video Technol 23(11):1957\u20131967","journal-title":"IEEE Trans Circ Syst Video Technol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09809-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09809-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09809-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T14:11:31Z","timestamp":1633702291000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09809-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,8]]},"references-count":66,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9809"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09809-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2020,10,8]]},"assertion":[{"value":"6 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}