{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:24:36Z","timestamp":1743002676826,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031368073"},{"type":"electronic","value":"9783031368080"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-36808-0_3","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:03:04Z","timestamp":1688079784000},"page":"32-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Siamese Network with\u00a0Gabor Filter for\u00a0Recognizing Handwritten Digits"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7643-4735","authenticated-orcid":false,"given":"Rauzan","family":"Sumara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6245-4946","authenticated-orcid":false,"given":"Ivan Luthfi","family":"Ihwani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Babu, U.R., Venkateswarlu, Y., Chintha, A.K.: Handwritten digit recognition using k-nearest neighbour classifier. In: Proceedings - 2014 World Congress on Computing and Communication Technologies, WCCCT 2014, pp. 60\u201365. IEEE Computer Society (2014)","DOI":"10.1109\/WCCCT.2014.7"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.patcog.2011.09.021","volume":"45","author":"XX Niu","year":"2012","unstructured":"Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recogn. 45, 1318\u20131325 (2012)","journal-title":"Pattern Recogn."},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neunet.2011.12.002","volume":"28","author":"A Goltsev","year":"2012","unstructured":"Goltsev, A., Gritsenko, V.: Investigation of efficient features for image recognition by neural networks. Neural Netw. 28, 15\u201323 (2012)","journal-title":"Neural Netw."},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Hossain, A., Ali, M.: Recognition of handwritten digit using convolutional neural network (CNN). In: Proceedings of 2020 International Conference on Computing and Data Science CDS 2020, vol. 19, pp. 183\u2013190 (2020)","DOI":"10.1109\/CDS49703.2020.00044"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Govindarajan, M.: Evaluation of ensemble classifiers for handwriting recognition. Int. J. Mod. Educ. Comput. Sci. 5, 11\u201320 (2013). https:\/\/doi.org\/10.5815\/ijmecs.2013.11.02","DOI":"10.5815\/ijmecs.2013.11.02"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Malik, H., Roy, N.: Extreme learning machine-based image classification model using handwritten digit database. In: Advances in Intelligent Systems and Computing, pp. 607\u2013618 (2018). https:\/\/doi.org\/10.1007\/978-981-13-1822-1_57","DOI":"10.1007\/978-981-13-1822-1_57"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2298\u20132304 (2017). https:\/\/doi.org\/10.1109\/tpami.2016.2646371","DOI":"10.1109\/TPAMI.2016.2646371"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Shamim, S.M., Miah, M.B.A., Sarker, A., Rana, M., Jobair, A.A.: handwritten digit recognition using machine learning algorithms. Indonesian J. Sci. Technol. 3, 29 (2018). https:\/\/doi.org\/10.17509\/ijost.v3i1.10795","DOI":"10.17509\/ijost.v3i1.10795"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Dine, K.Z., Nasri, M., Moussaoui, M., Benchaou, S., Aouinti, F.: Digit recognition using different features extraction methods. In: Advances in Intelligent Systems and Computing, pp. 167\u2013175 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46568-5_17","DOI":"10.1007\/978-3-319-46568-5_17"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Ji, P., Jin, L., Li, X.: Vision-based vehicle type classification using partial Gabor filter bank. In: Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2007, pp. 1037\u20131040 (2007)","DOI":"10.1109\/ICAL.2007.4338720"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Sarwar, S.S., Panda, P., Roy, K.: Gabor filter assisted energy efficient fast learning convolutional neural networks. In: Proceedings of International Symposium on Low Power Electronics and Designs, pp. 1\u20136 (2017)","DOI":"10.1109\/ISLPED.2017.8009202"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Alekseev, A., Bobe, A.: GaborNet: Gabor filters with learnable parameters in deep convolutional neural networks. arXiv. (2019)","DOI":"10.1109\/EnT47717.2019.9030571"},{"key":"3_CR13","unstructured":"Koch, G., Zemel, R., workshop, R.S.-I. deep learning, 2015, undefined: Siamese neural networks for one-shot image recognition. cs.toronto.edu. http:\/\/www.cs.toronto.edu\/~gkoch\/files\/msc-thesis.pdf. Accessed 09 Apr 2021"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Bromley, J., et al.: Signature verification using a \u201cSiamese\u201d time delay neural network. Int. J. Pattern Recognit. Artif. Intell. 07, 669\u2013688 (1993)","DOI":"10.1142\/S0218001493000339"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neucom.2017.02.029","volume":"241","author":"S Scardapane","year":"2017","unstructured":"Scardapane, S., Comminiello, D., Hussain, A., Uncini, A.: Group sparse regularization for deep neural networks. Neurocomputing. 241, 81\u201389 (2017)","journal-title":"Neurocomputing."},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"23 Kulkarni, S.R., Rajendran, B.: Spiking neural networks for handwritten digit recognition\u2013Supervised learning and network optimization. Neural Netw. 103, 118\u2013127 (2018)","DOI":"10.1016\/j.neunet.2018.03.019"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.neunet.2017.12.005","volume":"99","author":"SR Kheradpisheh","year":"2018","unstructured":"Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: STDP-based spiking deep convolutional neural networks for object recognition. Neural Netw. 99, 56\u201367 (2018)","journal-title":"Neural Netw."},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.neucom.2018.10.090","volume":"391","author":"MR Chen","year":"2020","unstructured":"Chen, M.R., Chen, B.P., Zeng, G.Q., Lu, K.D., Chu, P.: An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition. Neurocomputing 391, 260\u2013272 (2020)","journal-title":"Neurocomputing"},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.neucom.2019.10.083","volume":"378","author":"Y Yuan","year":"2020","unstructured":"Yuan, Y., Zhang, J., Wang, Q.: Deep Gabor convolution network for person re-identification. Neurocomputing 378, 387\u2013398 (2020)","journal-title":"Neurocomputing"},{"key":"3_CR20","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-1-0716-0826-5_3","volume":"2190","author":"D Chicco","year":"2021","unstructured":"Chicco, D.: Siamese neural networks: an overview. Methods Mol. Biol. 2190, 73\u201394 (2021)","journal-title":"Methods Mol. Biol."},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of 2005 IEEE Computer and Social Conference on Computer Vision and Pattern Recognition, CVPR 2005, I, pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: A unified embedding for face recognition and clustering. In: Proceedings of IEEE Computer and Social Conference on Computer Vision and Pattern Recognition 07\u201312-June-2015, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"3_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-01261-8_28","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Dong","year":"2018","unstructured":"Dong, X., Shen, J.: Triplet loss in Siamese network for object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 472\u2013488. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_28"},{"key":"3_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-24261-3_7","volume-title":"Similarity-Based Pattern Recognition","author":"E Hoffer","year":"2015","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84\u201392. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24261-3_7"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Lin, G., Shen, C., Reid, I.: Fast training of triplet-based deep binary embedding networks. In: Proceedings of IEEE Computer and Social Conference on Computer Vision and Pattern Recognition, 2016-December, pp. 5955\u20135964 (2016)","DOI":"10.1109\/CVPR.2016.641"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Song, H.O., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: Proceedings of IEEE Computer and Social Conference on Computer Vision and Pattern Recognition 2016-December, pp. 4004\u20134012 (2015)","DOI":"10.1109\/CVPR.2016.434"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: Proceedings of IEEE Computer and Social Conference on Computer Vision and Pattern Recognition, 2016-December, pp. 1335\u20131344 (2016)","DOI":"10.1109\/CVPR.2016.149"},{"key":"3_CR28","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In Defense of the Triplet Loss for Person Re-Identification (2017)"},{"key":"3_CR29","unstructured":"TensorFlow Addons Losses: TripletSemiHardLoss. https:\/\/www.tensorflow.org\/addons\/tutorials\/losses_triplet. Accessed 07 July 2022"},{"key":"3_CR30","unstructured":"LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, H., Liu, H.: Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granular Comput. 5, 411\u2013418 (2019). https:\/\/doi.org\/10.1007\/s41066-019-00158-6","DOI":"10.1007\/s41066-019-00158-6"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Enriquez, E.A., Gordillo, N., Bergasa, L.M., Romera, E., Hu\u00e9lamo, C.G.: Convolutional neural network vs traditional methods for offline recognition of handwritten digits. In: Advances in Intelligent Systems and Computing, pp. 87\u201399 (2018). https:\/\/doi.org\/10.1007\/978-3-319-99885-5_7","DOI":"10.1007\/978-3-319-99885-5_7"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36808-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T10:32:13Z","timestamp":1729679533000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36808-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031368073","9783031368080"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36808-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","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":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","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":"67","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":"13","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":"24% - 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":"2.5","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":"8,5","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)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}