{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:14:48Z","timestamp":1766268888736,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-024-09798-5","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T08:02:20Z","timestamp":1714377740000},"page":"10031-10054","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data augmentation based on shape space exploration for low-size datasets: application to 2D shape classification"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6179-1358","authenticated-orcid":false,"given":"Emna","family":"Ghorbel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faouzi","family":"Ghorbel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"key":"9798_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27\u201348","journal-title":"Neurocomputing"},{"issue":"11","key":"9798_CR2","doi-asserted-by":"publisher","first-page":"3542","DOI":"10.1016\/j.patcog.2015.04.018","volume":"48","author":"Y Li","year":"2015","unstructured":"Li Y, Wang S, Tian Q, Ding X (2015) Feature representation for statistical-learning-based object detection: a review. Pattern Recognit 48(11):3542\u20133559","journal-title":"Pattern Recognit"},{"key":"9798_CR3","doi-asserted-by":"crossref","unstructured":"Paulin M, Revaud J, Harchaoui Z, Perronnin F, Schmid C (2014) Transformation pursuit for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3646\u20133653","DOI":"10.1109\/CVPR.2014.466"},{"key":"9798_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.image.2019.03.009","volume":"75","author":"A Ben Khlifa","year":"2019","unstructured":"Ben Khlifa A, Ghorbel F (2019) An almost complete curvature scale space representation: Euclidean case. Signal Process Image Commun 75:32\u201343","journal-title":"Signal Process Image Commun"},{"key":"9798_CR5","unstructured":"Perez L, Wang J (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621"},{"key":"9798_CR6","doi-asserted-by":"crossref","unstructured":"Summers C, Dinneen MJ (2019) Improved mixed-example data augmentation. In: 2019 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1262\u20131270","DOI":"10.1109\/WACV.2019.00139"},{"key":"9798_CR7","unstructured":"Inoue H (2018) Data augmentation by pairing samples for images classification. arXiv preprint arXiv:1801.02929"},{"key":"9798_CR8","unstructured":"Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412"},{"key":"9798_CR9","unstructured":"Kim J-H, Choo W, Song HO (2020) Puzzle mix: exploiting saliency and local statistics for optimal mixup. In: International conference on machine learning. PMLR, pp 5275\u20135285"},{"key":"9798_CR10","unstructured":"Zou Y, Verma V, Mittal S, Tang WH, Pham H, Kannala J, Bengio Y, Solin A, Kawaguchi K (2023) Mixupe: understanding and improving mixup from directional derivative perspective. In: Uncertainty in artificial intelligence (2023). PMLR, pp 2597\u20132607"},{"key":"9798_CR11","doi-asserted-by":"crossref","unstructured":"Zhu J, Shi L, Yan J, Zha H (2020) Automix: mixup networks for sample interpolation via cooperative barycenter learning. In: Computer Vision\u2013ECCV 2020: 16th European conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part X 16. Springer, pp 633\u2013649","DOI":"10.1007\/978-3-030-58607-2_37"},{"key":"9798_CR12","unstructured":"Verma V, Lamb A, Beckham C, Najafi A, Mitliagkas I, Lopez-Paz D, Bengio Y (2019) Manifold mixup: better representations by interpolating hidden states. In: International conference on machine learning. PMLR, pp 6438\u20136447"},{"key":"9798_CR13","unstructured":"Kang G, Dong X, Zheng L, Yang Y (2017) Patchshuffle regularization. arXiv preprint arXiv:1707.07103"},{"key":"9798_CR14","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 13001\u201313008","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"9798_CR15","doi-asserted-by":"crossref","unstructured":"Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576","DOI":"10.1167\/16.12.326"},{"key":"9798_CR16","unstructured":"Konno T, Iwazume M (2018) Icing on the cake: an easy and quick post-learnig method you can try after deep learning. arXiv preprint arXiv:1807.06540"},{"key":"9798_CR17","unstructured":"Bowles C, Chen L, Guerrero R, Bentley P, Gunn R, Hammers A, Dickie DA, Hern\u00e1ndez MV, Wardlaw J, Rueckert D (2018) Gan augmentation: augmenting training data using generative adversarial networks. arXiv preprint arXiv:1810.10863 (2018)"},{"issue":"5","key":"9798_CR18","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su J, Vargas DV, Sakurai K (2019) One pixel attack for fooling deep neural networks. IEEE Trans Evolut Comput 23(5):828\u2013841","journal-title":"IEEE Trans Evolut Comput"},{"key":"9798_CR19","doi-asserted-by":"crossref","unstructured":"El-Sawy A, Hazem E-B, Loey M (2016) Cnn for handwritten Arabic digits recognition based on lenet-5. In: International conference on advanced intelligent systems and informatics. Springer, pp 566\u2013575","DOI":"10.1007\/978-3-319-48308-5_54"},{"key":"9798_CR20","doi-asserted-by":"publisher","unstructured":"Patel V, Mujumdar N, Balasubramanian P, Marvaniya S, Mittal A (2019) Data augmentation using part analysis for shape classification. In: 2019 IEEE winter conference on applications of computer vision (WACV), pp 1223\u20131232. https:\/\/doi.org\/10.1109\/WACV.2019.00135","DOI":"10.1109\/WACV.2019.00135"},{"key":"9798_CR21","doi-asserted-by":"crossref","unstructured":"Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 3642\u20133649","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"9798_CR22","unstructured":"Sato I, Nishimura H, Yokoi K (2015) Apac: augmented pattern classification with neural networks. arXiv preprint arXiv:1505.03229"},{"key":"9798_CR23","unstructured":"Yin D, Lopes RG, Shlens J, Cubuk ED, Gilmer J (2019) A Fourier perspective on model robustness in computer vision. arXiv preprint arXiv:1906.08988"},{"key":"9798_CR24","doi-asserted-by":"crossref","unstructured":"Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:1405.3531","DOI":"10.5244\/C.28.6"},{"key":"9798_CR25","unstructured":"Simard PY, Steinkraus D, Platt JC et\u00a0al (2003) Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol 3"},{"key":"9798_CR26","doi-asserted-by":"publisher","first-page":"5788","DOI":"10.1109\/TIP.2022.3199105","volume":"31","author":"E Ghorbel","year":"2022","unstructured":"Ghorbel E, Ghorbel F, M\u2019Hiri S (2022) A fast and efficient shape blending by stable and analytically invertible finite descriptors. IEEE Trans Image Process 31:5788\u20135800. https:\/\/doi.org\/10.1109\/TIP.2022.3199105","journal-title":"IEEE Trans Image Process"},{"issue":"02","key":"9798_CR27","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1142\/S0218654302000145","volume":"8","author":"T Surazhsky","year":"2002","unstructured":"Surazhsky T, Elber G (2002) Metamorphosis of planar parametric curves via curvature interpolation. Int J Shape Model 8(02):201\u2013216","journal-title":"Int J Shape Model"},{"key":"9798_CR28","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.cagd.2017.03.005","volume":"52","author":"M Hirano","year":"2017","unstructured":"Hirano M, Watanabe Y, Ishikawa M (2017) Rapid blending of closed curves based on curvature flow. Comput Aided Geom Design 52:217\u2013230","journal-title":"Comput Aided Geom Design"},{"issue":"5","key":"9798_CR29","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.gmod.2014.04.005","volume":"76","author":"M Saba","year":"2014","unstructured":"Saba M, Schneider T, Hormann K, Scateni R (2014) Curvature-based blending of closed planar curves. Graph Models 76(5):263\u2013272","journal-title":"Graph Models"},{"key":"9798_CR30","doi-asserted-by":"crossref","unstructured":"Sederberg TW, Gao P, Wang G, Mu H (1993) 2-d shape blending: an intrinsic solution to the vertex path problem. In: Proceedings of the 20th annual conference on computer graphics and interactive techniques. ACM, New York, NY, pp 15\u201318","DOI":"10.1145\/166117.166118"},{"issue":"3","key":"9798_CR31","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TPAMI.2004.1262333","volume":"26","author":"E Klassen","year":"2004","unstructured":"Klassen E, Srivastava A, Mio M, Joshi SH (2004) Analysis of planar shapes using geodesic paths on shape spaces. IEEE Trans Pattern Anal Mach Intell 26(3):372\u2013383","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"9798_CR32","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1109\/TPAMI.2010.184","volume":"33","author":"A Srivastava","year":"2010","unstructured":"Srivastava A, Klassen E, Joshi SH, Jermyn IH (2010) Shape analysis of elastic curves in Euclidean spaces. IEEE Trans Pattern Anal Mach Intell 33(7):1415\u20131428","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"9798_CR33","doi-asserted-by":"publisher","first-page":"17377","DOI":"10.1007\/s11042-020-09203-y","volume":"80","author":"L Jin","year":"2021","unstructured":"Jin L, Wen Z, Hu Z (2021) Topology-preserving nonlinear shape registration on the shape manifold. Multimed Tools Appl 80(11):17377\u201317389","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"9798_CR34","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/38.365005","volume":"15","author":"M Shapira","year":"1995","unstructured":"Shapira M, Rappoport A (1995) Shape blending using the star-skeleton representation. IEEE Comput Graph Appl 15(2):44\u201350","journal-title":"IEEE Comput Graph Appl"},{"issue":"3","key":"9798_CR35","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.cag.2009.03.007","volume":"33","author":"W Yang","year":"2009","unstructured":"Yang W, Feng J (2009) 2d shape morphing via automatic feature matching and hierarchical interpolation. Comput Graph 33(3):414\u2013423","journal-title":"Comput Graph"},{"issue":"2","key":"9798_CR36","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s00607-006-0198-7","volume":"79","author":"S Hahmann","year":"2007","unstructured":"Hahmann S, Bonneau G-P, Caramiaux B, Cornillac M (2007) Multiresolution morphing for planar curves. Computing 79(2):197\u2013209","journal-title":"Computing"},{"issue":"6","key":"9798_CR37","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1007\/s00371-014-0955-0","volume":"30","author":"W Yang","year":"2014","unstructured":"Yang W, Wang X, Wang G (2014) Part-to-part morphing for planar curves. Vis Comput 30(6):919\u2013928","journal-title":"Vis Comput"},{"key":"9798_CR38","doi-asserted-by":"crossref","unstructured":"Ghorbel E, Ghorbel F, Sakly I, M\u2019Hiri S (2021) Fast blending of planar shapes based on invariant invertible and stable descriptors. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 10259\u201310265","DOI":"10.1109\/ICPR48806.2021.9412686"},{"key":"9798_CR39","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp 13001\u201313008","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"9798_CR40","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2018) Autoaugment: learning augmentation policies from data. arXiv preprint arXiv:1805.09501","DOI":"10.1109\/CVPR.2019.00020"},{"key":"9798_CR41","doi-asserted-by":"crossref","unstructured":"Jiang X, Bunke H, Abegglen K, Kandel A (2002) Curve morphing by weighted mean of strings. In: Object recognition supported by user interaction for service robots, vol 4. IEEE, New York City at 3 Park Ave, pp 192\u2013195","DOI":"10.1109\/ICPR.2002.1047430"},{"issue":"6","key":"9798_CR42","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1109\/TSMC.1982.4308918","volume":"12","author":"TR Crimmins","year":"1982","unstructured":"Crimmins TR (1982) A complete set of Fourier descriptors for two-dimensional shapes. IEEE Trans Syst Man Cybern 12(6):848\u2013855","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"9798_CR43","doi-asserted-by":"crossref","unstructured":"Ghorbel F (1992) Stability of invariant fourier descriptors and its inference in the shape classification. In: International conference on pattern recognition. IEEE Computer Society Press, New York City at 3 Park Ave, pp 130\u2013130","DOI":"10.1109\/ICPR.1992.201944"},{"key":"9798_CR44","first-page":"1","volume":"11","author":"S Elghoul","year":"2021","unstructured":"Elghoul S, Ghorbel F (2021) A fast and robust affine-invariant method for shape registration under partial occlusion. Int J Multimed Inf Retr 11:1\u201321","journal-title":"Int J Multimed Inf Retr"},{"issue":"1","key":"9798_CR45","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0020-0190(97)00140-3","volume":"64","author":"E Belogay","year":"1997","unstructured":"Belogay E, Cabrelli C, Molter U, Shonkwiler R (1997) Calculating the hausdorff distance between curves. Inf Process Lett 64(1):17\u201322","journal-title":"Inf Process Lett"},{"key":"9798_CR46","doi-asserted-by":"crossref","unstructured":"Latecki LJ, Lakamper R, Eckhardt T (2000) Shape descriptors for non-rigid shapes with a single closed contour. In: Proceedings IEEE conference on computer vision and pattern recognition. CVPR 2000 (Cat. No.PR00662), vol 1. IEEE, New York City at 3 Park Ave, pp 424\u20134291","DOI":"10.1109\/CVPR.2000.855850"},{"issue":"4","key":"9798_CR47","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551","journal-title":"Neural Comput"},{"key":"9798_CR48","unstructured":"Yuan A, Bai G, Jiao L, Liu Y (2012) Offline handwritten English character recognition based on convolutional neural network. In: 2012 10th IAPR international workshop on document analysis systems. IEEE, New York City at 3 Park Ave, pp 125\u2013129"},{"key":"9798_CR49","doi-asserted-by":"crossref","unstructured":"Ozdemir MA, Elagoz B, Alaybeyoglu A, Sadighzadeh R, Akan A (2019) Real time emotion recognition from facial expressions using CNN architecture. In: 2019 Medical technologies congress (tiptekno). IEEE, New York City at 3 Park Ave, pp 1\u20134","DOI":"10.1109\/TIPTEKNO.2019.8895215"},{"key":"9798_CR50","doi-asserted-by":"crossref","unstructured":"Kayed M, Anter A, Mohamed H (2020) Classification of garments from fashion mnist dataset using CNN lenet-5 architecture. In: 2020 international conference on innovative trends in communication and computer engineering (ITCE). IEEE, pp 238\u2013243","DOI":"10.1109\/ITCE48509.2020.9047776"},{"key":"9798_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"9798_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9798_CR53","unstructured":"Sato I, Nishimura H, Yokoi K (2015) Apac: Augmented pattern classification with neural networks. arXiv preprint arXiv:1505.03229"},{"issue":"6","key":"9798_CR54","doi-asserted-by":"publisher","first-page":"2116","DOI":"10.1016\/j.patcog.2013.12.008","volume":"47","author":"X Wang","year":"2014","unstructured":"Wang X, Feng B, Bai X, Liu W, Latecki LJ (2014) Bag of contour fragments for robust shape classification. Pattern Recognit 47(6):2116\u20132125","journal-title":"Pattern Recognit"},{"key":"9798_CR55","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.patrec.2016.02.002","volume":"83","author":"W Shen","year":"2016","unstructured":"Shen W, Jiang Y, Gao W, Zeng D, Wang X (2016) Shape recognition by bag of skeleton-associated contour parts. Pattern Recognit Lett 83:321\u2013329","journal-title":"Pattern Recognit Lett"},{"key":"9798_CR56","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09798-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09798-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09798-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:21:22Z","timestamp":1716798082000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09798-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":56,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9798"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09798-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"20 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}