{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T07:46:04Z","timestamp":1726040764096},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030272715"},{"type":"electronic","value":"9783030272722"}],"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\/978-3-030-27272-2_8","type":"book-chapter","created":{"date-parts":[[2019,8,11]],"date-time":"2019-08-11T23:02:59Z","timestamp":1565564579000},"page":"94-105","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Variational Learning of Finite Generalized Inverted Dirichlet Mixture Models with Feature Selection and Component Splitting"],"prefix":"10.1007","author":[{"given":"Kamal","family":"Maanicshah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samr","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nizar","family":"Bouguila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,3]]},"reference":[{"issue":"3","key":"8_CR1","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346\u2013359 (2008). Similarity Matching in Computer Vision and Multimedia","journal-title":"Comput. Vis. Image Underst."},{"issue":"3","key":"8_CR2","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s10489-015-0714-6","volume":"44","author":"T Bdiri","year":"2016","unstructured":"Bdiri, T., Bouguila, N., Ziou, D.: Variational bayesian inference for infinite generalized inverted dirichlet mixtures with feature selection and its application to clustering. Appl. Intell. 44(3), 507\u2013525 (2016)","journal-title":"Appl. Intell."},{"key":"8_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/978-3-642-55032-4_29","volume-title":"Information and Communication Technology, ICT-EurAsia","author":"N Bouguila","year":"2014","unstructured":"Bouguila, N., Mashrgy, M.A.: An infinite mixture model of generalized inverted dirichlet distributions for high-dimensional positive data modeling. In: Linawati, M.M.S., Neuhold, E.J., Tjoa, A.M., You, I. (eds.) Information and Communication Technology, ICT-EurAsia. Lecture Notes in Computer Science, vol. 8407, pp. 296\u2013305. Springer, Heidelberg (2014). \n                      https:\/\/doi.org\/10.1007\/978-3-642-55032-4_29"},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TIP.2004.834664","volume":"13","author":"N Bouguila","year":"2004","unstructured":"Bouguila, N., Ziou, D., Vaillancourt, J.: Unsupervised learning of a finite mixture model based on the dirichlet distribution and its application. IEEE Tran. Image Process. 13, 1533\u20131543 (2004)","journal-title":"IEEE Tran. Image Process."},{"issue":"8","key":"8_CR5","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1109\/TPAMI.2008.155","volume":"31","author":"S Boutemedjet","year":"2009","unstructured":"Boutemedjet, S., Bouguila, N., Ziou, D.: A hybrid feature extraction selection approach for high-dimensional non-gaussian data clustering. IEEE Trans. Pattern Anal. Mach. Intell. 31(8), 1429\u20131443 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"8_CR6","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1109\/TNN.2006.891114","volume":"18","author":"C Constantinopoulos","year":"2007","unstructured":"Constantinopoulos, C., Likas, A.: Unsupervised learning of gaussian mixtures based on variational component splitting. IEEE Trans. Neural Networks 18(3), 745\u2013755 (2007)","journal-title":"IEEE Trans. Neural Networks"},{"key":"8_CR7","unstructured":"Corduneanu., A., Bishop, C.M.: Variational Bayesian model selection for mixture distributions. In: Proceedings Eighth International Conference on Artificial Intelligence and Statistics (2001)"},{"key":"8_CR8","unstructured":"Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints (2004)"},{"key":"8_CR9","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings IEEE Computer Society Conference Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886\u2013893, June 2005"},{"key":"8_CR10","unstructured":"Fan, W., Bouguila, N.: A variational component splitting approach for finite generalized dirichlet mixture models. In: 2012 International Conference on Communications and Information Technology (ICCIT), pp. 53\u201357, June 2012"},{"issue":"10","key":"8_CR11","doi-asserted-by":"publisher","first-page":"2754","DOI":"10.1016\/j.patcog.2013.03.026","volume":"46","author":"W Fan","year":"2013","unstructured":"Fan, W., Bouguila, N.: Variational learning of a dirichlet process of generalized dirichlet distributions for simultaneous clustering and feature selection. Pattern Recogn. 46(10), 2754\u20132769 (2013)","journal-title":"Pattern Recogn."},{"issue":"2","key":"8_CR12","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91 (2004)","journal-title":"Int. J. Comput. Vis."},{"issue":"11","key":"8_CR13","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.1109\/TPAMI.2011.63","volume":"33","author":"Z Ma","year":"2011","unstructured":"Ma, Z., Leijon, A.: Bayesian estimation of beta mixture models with variational inference. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2160\u20132173 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR14","volume-title":"Tutorial on Variational Approximation Methods. Neural Information Processing","author":"M Opper","year":"2001","unstructured":"Opper, M., Saad, D.: Tutorial on Variational Approximation Methods. Neural Information Processing. Institute of Technology Press, Cambridge (2001)"},{"issue":"12","key":"8_CR15","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1016\/j.patrec.2010.05.009","volume":"31","author":"R P\u00e9teri","year":"2010","unstructured":"P\u00e9teri, R., Fazekas, S., Huiskes, M.J.: DynTex: a comprehensive database of dynamic textures. Pattern Recogn. Lett. 31(12), 1627\u20131632 (2010)","journal-title":"Pattern Recogn. Lett."}],"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\/978-3-030-27272-2_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,11]],"date-time":"2019-08-11T23:06:07Z","timestamp":1565564767000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-27272-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030272715","9783030272722"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-27272-2_8","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":"3 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)"}}]}}