{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:47:05Z","timestamp":1726040825218},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030272012"},{"type":"electronic","value":"9783030272029"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-27202-9_24","type":"book-chapter","created":{"date-parts":[[2019,8,11]],"date-time":"2019-08-11T23:02:59Z","timestamp":1565564579000},"page":"264-274","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bayesian Learning of Infinite Asymmetric Gaussian Mixture Models for Background Subtraction"],"prefix":"10.1007","author":[{"given":"Ziyang","family":"Song","sequence":"first","affiliation":[]},{"given":"Samr","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Nizar","family":"Bouguila","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,8]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Sakpal, N.S., Sabnis, M.: Adaptive background subtraction in images. In: 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), pp. 439\u2013444, February 2018","DOI":"10.1109\/ICACCT.2018.8529323"},{"key":"24_CR2","unstructured":"Fan, W., Bouguila, N.: Video background subtraction using online infinite dirichlet mixture models. In: 21st European Signal Processing Conference, EUSIPCO 2013, Marrakech, Morocco, 9\u201313 September, pp. 1\u20135 (2013)"},{"issue":"5","key":"24_CR3","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s00138-013-0568-z","volume":"25","author":"T Elguebaly","year":"2014","unstructured":"Elguebaly, T., Bouguila, N.: Background subtraction using finite mixtures of asymmetric gaussian distributions and shadow detection. Mach. Vis. Appl. 25(5), 1145\u20131162 (2014)","journal-title":"Mach. Vis. Appl."},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Tang, C., Ahmad, M.O., Wang, C.: Foreground segmentation in video sequences with a dynamic background. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/CISP-BMEI.2018.8633130"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Hedayati, M., Zaki, W.M.D.W., Hussain, A.: Real-time background subtraction for video surveillance: from research to reality. In: 2010 6th International Colloquium on Signal Processing its Applications, pp. 1\u20136, May 2010","DOI":"10.1109\/CSPA.2010.5545277"},{"issue":"12","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1916","DOI":"10.1016\/j.patrec.2005.03.016","volume":"26","author":"N Bouguila","year":"2005","unstructured":"Bouguila, N., Ziou, D.: Using unsupervised learning of a finite dirichlet mixture model to improve pattern recognition applications. Pattern Recogn. Lett. 26(12), 1916\u20131925 (2005)","journal-title":"Pattern Recogn. Lett."},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Bilge, Y.C., Kaya, F., Cinbis, N.,\u00c7elikcan, U., Sever, H.: Anomaly detection using improved background subtraction. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1\u20134, May 2017","DOI":"10.1109\/SIU.2017.7960592"},{"key":"24_CR8","unstructured":"Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 2, pp. 246\u2013252. IEEE (1999)"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: null, pp. 28\u201331. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1333992"},{"issue":"1","key":"24_CR10","first-page":"129","volume":"2","author":"D Wang","year":"2005","unstructured":"Wang, D., Xie, W., Pei, J., Lu, Z.: Moving area detection based on estimation of static background. J. Inf. Comput. Sci. 2(1), 129\u2013134 (2005)","journal-title":"J. Inf. Comput. Sci."},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Fu, S., Bouguila, N.: A bayesian intrusion detection framework. In: 2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp. 1\u20138 (2018)","DOI":"10.1109\/CyberSecPODS.2018.8560681"},{"issue":"8","key":"24_CR12","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/TKDE.2006.133","volume":"18","author":"N Bouguila","year":"2006","unstructured":"Bouguila, N., Ziou, D.: Unsupervised selection of a finite dirichlet mixture model: an mml-based approach. IEEE Trans. Knowl. Data Eng. 18(8), 993\u20131009 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR13","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.imavis.2014.10.011","volume":"34","author":"T Elguebaly","year":"2015","unstructured":"Elguebaly, T., Bouguila, N.: Simultaneous high-dimensional clustering and feature selection using asymmetric gaussian mixture models. Image Vision Comput. 34, 27\u201341 (2015)","journal-title":"Image Vision Comput."},{"issue":"3","key":"24_CR14","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1111\/j.2517-6161.1995.tb02042.x","volume":"57","author":"BP Carlin","year":"1995","unstructured":"Carlin, B.P., Chib, S.: Bayesian model choice via markov chain monte carlo methods. J. Roy. Stat. Soc.: Ser. B (Methodol.) 57(3), 473\u2013484 (1995)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Elguebaly, T., Bouguila, N.: A nonparametric bayesian approach for enhanced pedestrian detection and foreground segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2011, Colorado Springs, CO, USA, 20\u201325 June, pp. 21\u201326 (2011)","DOI":"10.1109\/CVPRW.2011.5981800"},{"key":"24_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-642-21593-3_21","volume-title":"Image Analysis and Recognition","author":"Tarek Elguebaly","year":"2011","unstructured":"Elguebaly, Tarek, Bouguila, Nizar: Infinite generalized gaussian mixture modeling and applications. In: Kamel, Mohamed, Campilho, Aur\u00e9lio (eds.) ICIAR 2011. LNCS, vol. 6753, pp. 201\u2013210. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21593-3_21"},{"issue":"2","key":"24_CR17","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s10115-011-0467-4","volume":"33","author":"N Bouguila","year":"2012","unstructured":"Bouguila, N., Ziou, D.: A countably infinite mixture model for clustering and feature selection. Knowl. Inf. Syst. 33(2), 351\u2013370 (2012)","journal-title":"Knowl. Inf. Syst."},{"issue":"2","key":"24_CR18","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.patrec.2011.09.037","volume":"33","author":"N Bouguila","year":"2012","unstructured":"Bouguila, N.: Infinite liouville mixture models with application to text and texture categorization. Pattern Recogn. Lett. 33(2), 103\u2013110 (2012)","journal-title":"Pattern Recogn. Lett."},{"key":"24_CR19","first-page":"554","volume-title":"Advances in Neural Information Processing Systems","author":"CE Rasmussen","year":"2000","unstructured":"Rasmussen, C.E.: The infinite gaussian mixture model. In: Solla, S.A., Leen, T.K., M\u00fcller, K. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 554\u2013560. MIT Press, Cambridge (2000)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 387\u2013394 (2014)","DOI":"10.1109\/CVPRW.2014.126"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-27202-9_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T22:16:56Z","timestamp":1721600216000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-27202-9_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030272012","9783030272029"],"references-count":20,"URL":"http:\/\/dx.doi.org\/10.1007\/978-3-030-27202-9_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"8 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Waterloo, ON","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciar2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aimiconf.org\/iciar19\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfTool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"142","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}
