{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:53:04Z","timestamp":1743137584149,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030313319"},{"type":"electronic","value":"9783030313326"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-31332-6_42","type":"book-chapter","created":{"date-parts":[[2019,9,21]],"date-time":"2019-09-21T15:02:34Z","timestamp":1569078154000},"page":"485-497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-label Logo Classification Using Convolutional Neural Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-6886","authenticated-orcid":false,"given":"Antonio-Javier","family":"Gallego","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9445-5529","authenticated-orcid":false,"given":"Antonio","family":"Pertusa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marisa","family":"Bernabeu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,22]]},"reference":[{"key":"42_CR1","unstructured":"Chiam, J.H.: Brand logo classification. Technical report, Stanford University (2015)"},{"issue":"11","key":"42_CR2","doi-asserted-by":"publisher","first-page":"2086","DOI":"10.3390\/app8112086","volume":"8","author":"AJ Gallego","year":"2018","unstructured":"Gallego, A.J., Pertusa, A., Calvo-Zaragoza, J.: Improving convolutional neural networks\u2019 accuracy in noisy environments using k-nearest neighbors. Appl. Sci. 8(11), 2086 (2018)","journal-title":"Appl. Sci."},{"issue":"12","key":"42_CR3","first-page":"13","volume":"118","author":"S Ghosh","year":"2015","unstructured":"Ghosh, S., Parekh, R.: Automated color logo recognition system based on shape and color features. Int. J. Comput. Appl. 118(12), 13\u201320 (2015)","journal-title":"Int. J. Comput. Appl."},{"key":"42_CR4","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/978-3-319-76348-4_16","volume-title":"Intelligent Systems Design and Applications","author":"DS Guru","year":"2018","unstructured":"Guru, D.S., Vinay Kumar, N.: Interval valued feature selection for classification of logo images. In: Abraham, A., Muhuri, P.K., Muda, A.K., Gandhi, N. (eds.) ISDA 2017. AISC, vol. 736, pp. 154\u2013165. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76348-4_16"},{"key":"42_CR5","unstructured":"Iandola, F.N., Shen, A., Gao, P., Keutzer, K.: DeepLogo: hitting logo recognition with the deep neural network hammer. CoRR abs\/1510.02131 (2015)"},{"key":"42_CR6","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.procs.2016.05.245","volume":"85","author":"N. Vinay Kumar","year":"2016","unstructured":"Kumar, N.V., Pratheek, Kantha, V.V., Govindaraju, K., Guru, D.: Features fusion for classification of logos. In: Internetional Conference on Computational Modelling and Security (CMS), vol. 85, pp. 370\u2013379 (2016)","journal-title":"Procedia Computer Science"},{"issue":"Nov","key":"42_CR7","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Perez, C.A., et al.: Trademark image retrieval using a combination of deep convolutional neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137, July 2018","DOI":"10.1109\/IJCNN.2018.8489045"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Pornpanomchai, C., Boonsripornchai, P., Puttong, P., Rattananirundorn, C.: Logo recognition system. In: ICSEC 2015, pp. 1\u20136 (2015)","DOI":"10.1109\/ICSEC.2015.7401394"},{"issue":"6","key":"42_CR10","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1016\/j.patcog.2010.01.007","volume":"43","author":"H Qi","year":"2010","unstructured":"Qi, H., Li, K., Shen, Y., Qu, W.: An effective solution for trademark image retrieval by combining shape description and feature matching. Pattern Recogn. 43(6), 2017\u20132027 (2010)","journal-title":"Pattern Recogn."},{"key":"42_CR11","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-0-387-09823-4_34","volume-title":"Data Mining and Knowledge Discovery Handbook","author":"Grigorios Tsoumakas","year":"2009","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667\u2013685 (2010)"},{"key":"42_CR12","unstructured":"Tursun, O., Aker, C., Kalkan, S.: A large-scale dataset and benchmark for similar trademark retrieval. CoRR abs\/1701.05766 (2017)"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"T\u00fczk\u00f6, A., Herrmann, C., Manger, D., Beyerer, J.: Open set logo detection and retrieval. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (2018)","DOI":"10.5220\/0006614602840292"},{"key":"42_CR14","unstructured":"World Intellectual Property Organization: International Classification of the Figurative Elements of Marks: (Vienna Classification). WIPO Publication, World Intellectual Property Organization (2002)"},{"key":"42_CR15","unstructured":"Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs\/1212.5701 (2012)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-31332-6_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T00:11:04Z","timestamp":1695255064000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-31332-6_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030313319","9783030313326"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-31332-6_42","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":"22 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"1 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ibpria.org\/2019\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"137","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":"99","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":"0","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":"72% - 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":"3.1","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}