{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:44:53Z","timestamp":1743003893478,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030208721"},{"type":"electronic","value":"9783030208738"}],"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\/978-3-030-20873-8_23","type":"book-chapter","created":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T16:32:03Z","timestamp":1558801923000},"page":"357-370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Embedding Using Bayesian Risk Minimization with Application to Sketch Recognition"],"prefix":"10.1007","author":[{"given":"Anand","family":"Mishra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajeet Kumar","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,26]]},"reference":[{"key":"23_CR1","unstructured":"Bellet, A., Habrard, A., Sebban, M.: A survey on metric learning for feature vectors and structured data. CoRR abs\/1306.6709 (2013). \n                      http:\/\/arxiv.org\/abs\/1306.6709"},{"issue":"3","key":"23_CR2","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"key":"23_CR3","volume-title":"Pattern Classification","author":"RO Duda","year":"2001","unstructured":"Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2001)"},{"issue":"4","key":"23_CR4","first-page":"44","volume":"31","author":"M Eitz","year":"2012","unstructured":"Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31(4), 44 (2012)","journal-title":"ACM Trans. Graph."},{"key":"23_CR5","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR (2006)"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"He, J., Wu, X., Jiang, Y., Zhao, B., Peng, Q.: Sketch recognition with deep visual-sequential fusion model. In: ACM-MM (2017)","DOI":"10.1145\/3123266.3123321"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR8","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)"},{"key":"23_CR9","unstructured":"Lahlali, S.E., Sadiq, A., Mbarki, S.: A review of face sketch recognition systems. J. Theor. Appl. Inf. Technol. 81(2) (2015)"},{"key":"23_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2015.02.003","volume":"137","author":"Y Li","year":"2015","unstructured":"Li, Y., Hospedales, T.M., Song, Y., Gong, S.: Free-hand sketch recognition by multi-kernel feature learning. Comput. Vis. Image Underst. 137, 1\u201311 (2015)","journal-title":"Comput. Vis. Image Underst."},{"issue":"10","key":"23_CR11","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1016\/j.cad.2004.11.004","volume":"37","author":"T Lu","year":"2005","unstructured":"Lu, T., Tai, C., Su, F., Cai, S.: A new recognition model for electronic architectural drawings. Comput. Aided Des. 37(10), 1053\u20131069 (2005)","journal-title":"Comput. Aided Des."},{"key":"23_CR12","unstructured":"Manmatha, R., Wu, C., Smola, A.J., Kr\u00e4henb\u00fchl, P.: Sampling matters in deep embedding learning. In: ICCV (2017)"},{"key":"23_CR13","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS-W (2017)"},{"issue":"4","key":"23_CR14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1145\/2897824.2925954","volume":"35","author":"P Sangkloy","year":"2016","unstructured":"Sangkloy, P., Burnell, N., Ham, C., Hays, J.: The sketchy database: learning to retrieve badly drawn bunnies. ACM Trans. Graph. 35(4), 119 (2016)","journal-title":"ACM Trans. Graph."},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Sarvadevabhatla, R.K., Kundu, J., Babu, R.V.: Enabling my robot to play pictionary: recurrent neural networks for sketch recognition. In: ACM-MM (2016)","DOI":"10.1145\/2964284.2967220"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Sarvadevabhatla, R.K., Kundu, J., Babu, R.V.: Enabling my robot to play pictionary: recurrent neural networks for sketch recognition (2016)","DOI":"10.1145\/2964284.2967220"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Schneider, R., Tuytelaars, T.: Sketch classification and classification-driven analysis using fisher vectors 33(11), 174:1\u2013174:9 (2014)","DOI":"10.1145\/2661229.2661231"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Schneider, R.G., Tuytelaars, T.: Sketch classification and classification-driven analysis using fisher vectors. In: SIGGRAPH (2014)","DOI":"10.1145\/2661229.2661231"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"23_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint \n                      arXiv:1409.1556\n                      \n                     (2014)"},{"key":"23_CR21","unstructured":"Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: NIPS (2016)"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Song, H.O., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.434"},{"issue":"1","key":"23_CR23","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TCSVT.2003.818353","volume":"14","author":"X Tang","year":"2004","unstructured":"Tang, X., Wang, X.: Face sketch recognition. IEEE Trans. Circuit Syst. Video Technol. 14(1), 50\u201357 (2004)","journal-title":"IEEE Trans. Circuit Syst. Video Technol."},{"key":"23_CR24","unstructured":"Ustinova, E., Lempitsky, V.S.: Learning deep embeddings with histogram loss. In: NIPS (2016)"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.180"},{"key":"23_CR26","unstructured":"Wang, N., Li, J., Sun, L., Song, B., Gao, X.: Training-free synthesized face sketch recognition using image quality assessment metrics. arXiv preprint \n                      arXiv:1603.07823\n                      \n                     (2016)"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Xu, D., Song, J., Alameda-Pineda, X., Ricci, E., Sebe, N.: Multi-paced dictionary learning for cross-domain retrieval and recognition. In: ICPR (2016)","DOI":"10.1109\/ICPR.2016.7900132"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Xu, P., et al.: SketchMate: deep hashing for million-scale human sketch retrieval. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00844"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Li, Y., Song, Y.-Z., Gong, S.: Sketch recognition by ensemble matching of structured features. In: Proceedings of the British Machine Vision Conference (2013). (QMUL)","DOI":"10.5244\/C.27.35"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Yu, Q., Yang, Y., Liu, F., Song, Y., Xiang, T., Hospedales, T.M.: Sketch-a-Net: a deep neural network that beats humans. Int. J. Comput. Vis. 122(3) 411\u2013425 (2017)","DOI":"10.1007\/s11263-016-0932-3"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Yu, Q., Yang, Y., Song, Y., Xiang, T., Hospedales, T.M.: Sketch-a-Net that beats humans. In: BMVC (2015)","DOI":"10.5244\/C.29.7"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Zitnick, C.L., Parikh, D.: Bringing semantics into focus using visual abstraction. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 23\u201328 June 2013, pp. 3009\u20133016 (2013)","DOI":"10.1109\/CVPR.2013.387"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2018"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20873-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,26]],"date-time":"2019-05-26T02:47:44Z","timestamp":1558838864000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20873-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030208721","9783030208738"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20873-8_23","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":"26 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2018.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"979","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"274","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28% - 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"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}