{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:56:20Z","timestamp":1768690580259,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"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":["Evol. Intel."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s12065-022-00734-x","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T15:03:48Z","timestamp":1654268628000},"page":"1237-1258","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An improved image clustering algorithm based on Kernel method and Tchebychev orthogonal moments"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4070-4964","authenticated-orcid":false,"given":"Souad","family":"Azzouzi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amal","family":"Hjouji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaouad","family":"EL-Mekkaoui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"EL Khalfi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,3]]},"reference":[{"issue":"1","key":"734_CR1","doi-asserted-by":"publisher","first-page":"e0210236","DOI":"10.1371\/journal.pone.0210236","volume":"14","author":"MZ Rodriguez","year":"2019","unstructured":"Rodriguez MZ et al (2019) Clustering algorithms: a comparative approach. PLoS ONE 14(1):e0210236","journal-title":"PLoS ONE"},{"key":"734_CR2","unstructured":"Clustering algorithm-an overview Science direct topics. https:\/\/www.sciencedirect.com\/topics\/engineering\/clustering-algorithm"},{"key":"734_CR3","doi-asserted-by":"crossref","unstructured":"Aslam Y, Santhi N, Ramasamy N, Ramar K (2020) A review on various clustering approaches for image segmentation. In 2020 fourth international conference on inventive systems and control (ICISC), pp 679\u2013685, 8 Jan 2020","DOI":"10.1109\/ICISC47916.2020.9171125"},{"key":"734_CR4","first-page":"154","volume":"5","author":"K Rajput","year":"2017","unstructured":"Rajput K, Oza B (2017) A comparative study of classification techniques in data mining. Int J Creat Res Thoughts 5:154\u2013163","journal-title":"Int J Creat Res Thoughts"},{"issue":"2","key":"734_CR5","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1007\/s10462-018-9627-1","volume":"52","author":"A P\u00e9rez-Su\u00e1rez","year":"2019","unstructured":"P\u00e9rez-Su\u00e1rez A, Mart\u00ednez-Trinidad JF, Carrasco-Ochoa JA (2019) A review of conceptual clustering algorithms. Artif Intell Rev 52(2):1267\u20131296","journal-title":"Artif Intell Rev"},{"key":"734_CR6","first-page":"237","volume":"21","author":"A Alam","year":"2021","unstructured":"Alam A, Muqeem M, Ahmad S (2021) Comprehensive review on clustering techniques and its application on high dimensional data. Int J Comput Sci Netw Secur 21:237","journal-title":"Int J Comput Sci Netw Secur"},{"key":"734_CR7","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2020.0111218","author":"M Faizan","year":"2020","unstructured":"Faizan M, Zuhairi MF, Ismail S, Sultan S (2020) Applications of clustering techniques in data mining: a comparative study. Int J Adv Comput Sci Appl (IJACSA). https:\/\/doi.org\/10.14569\/IJACSA.2020.0111218","journal-title":"Int J Adv Comput Sci Appl (IJACSA)"},{"key":"734_CR8","doi-asserted-by":"crossref","unstructured":"Gan G, Ma C, Wu J (2020) Data clustering: theory, algorithms, and applications. Soc Ind Appl Math","DOI":"10.1137\/1.9781611976335"},{"issue":"1","key":"734_CR9","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MSMC.2015.2395653","volume":"1","author":"R Seising","year":"2015","unstructured":"Seising R (2015) On the history of fuzzy clustering: an interview with Jim Bezdek and Enrique Ruspini [History]. IEEE Syst Man Cybern Mag 1(1):20\u201348","journal-title":"IEEE Syst Man Cybern Mag"},{"issue":"1","key":"734_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/MCI.2018.2881643","volume":"14","author":"EH Ruspini","year":"2019","unstructured":"Ruspini EH, Bezdek JC, Keller JM (2019) Fuzzy clustering: a historical perspective. IEEE Comput Intell Mag 14(1):45\u201355","journal-title":"IEEE Comput Intell Mag"},{"key":"734_CR11","unstructured":"Kumar S (2020) Understanding K-means, K-means++ and, K-medoids Clustering Algorithms. Toward data science 11. https:\/\/towardsdatascience.com\/understanding-k-means-k-means-and-k-medoids-clustering-algorithms-ad9c9fbf47ca. Accessed 15 Apr 2021"},{"key":"734_CR12","unstructured":"Keshava Reddy K, Mrudula K (2017) Hard and fuzzy clustering methods: a comparative study"},{"key":"734_CR13","unstructured":"Rajan S (2020) Overview of clustering algorithms. https:\/\/towardsdatascience.com\/overview-of-clustering-algorithms-27e979e3724d. Accessed 7 Apr 2022"},{"key":"734_CR14","unstructured":"Fuzzy clustering algorithm-an overview. Science direct topics. https:\/\/www.sciencedirect.com\/topics\/computer-science\/fuzzy-clustering-algorithm"},{"key":"734_CR15","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.procs.2021.04.024","volume":"184","author":"C Djellali","year":"2021","unstructured":"Djellali C, Moutacalli MT (2021) A comparative study on fuzzy clustering for cloud computing taking web service as a case. Procedia Comput Sci 184:622\u2013627","journal-title":"Procedia Comput Sci"},{"key":"734_CR16","doi-asserted-by":"crossref","unstructured":"Gibran M, Nababan E, Sihombing P (2020) Analysis of face recognition with fuzzy C-means clustering image segmentation and learning vector quantization. In 2020 3rd international conference on mechanical, electronics, computer, and industrial technology (MECnIT), pp 188\u2013193, June 2020","DOI":"10.1109\/MECnIT48290.2020.9166649"},{"issue":"9","key":"734_CR17","first-page":"2103","volume":"12","author":"VR Sajja","year":"2021","unstructured":"Sajja VR, Kalluri HK (2021) Classification of brain tumors using fuzzy C-means and VGG16. Turk J Comput Math Educ (TURCOMAT) 12(9):2103\u20132113","journal-title":"Turk J Comput Math Educ (TURCOMAT)"},{"issue":"3","key":"734_CR18","first-page":"13","volume":"6","author":"Z Cebeci","year":"2015","unstructured":"Cebeci Z, Yildiz F (2015) Comparison of K-means and fuzzy C-means algorithms on different cluster structures. J Agric Inf 6(3):13\u201323","journal-title":"J Agric Inf"},{"key":"734_CR19","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2013.040406","author":"S Ghosh","year":"2013","unstructured":"Ghosh S, Dubey SK (2013) Comparative analysis of K-means and fuzzy C-means algorithms. Int J Adv Comput Sci Appl (IJACSA). https:\/\/doi.org\/10.14569\/IJACSA.2013.040406","journal-title":"Int J Adv Comput Sci Appl (IJACSA)"},{"issue":"1","key":"734_CR20","doi-asserted-by":"publisher","first-page":"012137","DOI":"10.1088\/1742-6596\/1453\/1\/012137","volume":"1453","author":"S Deng","year":"2020","unstructured":"Deng S (2020) Clustering with fuzzy C-means and common challenges. J Phys Conf Ser 1453(1):012137","journal-title":"J Phys Conf Ser"},{"issue":"4","key":"734_CR21","doi-asserted-by":"publisher","first-page":"2760","DOI":"10.11591\/ijece.v9i4.pp2760-2770","volume":"9","author":"KV Rajkumar","year":"2019","unstructured":"Rajkumar KV, Yesubabu A, Subrahmanyam K (2019) Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset. Int J Electr Comput Eng (IJECE) 9(4):2760","journal-title":"Int J Electr Comput Eng (IJECE)"},{"issue":"5","key":"734_CR22","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.1016\/j.patcog.2013.11.031","volume":"47","author":"P-L Lin","year":"2014","unstructured":"Lin P-L, Huang P-W, Kuo CH, Lai YH (2014) A size-insensitive integrity-based fuzzy c-means method for data clustering. Pattern Recognit 47(5):2042\u20132056","journal-title":"Pattern Recognit"},{"issue":"1","key":"734_CR23","first-page":"92","volume":"19","author":"O Ozdemir","year":"2019","unstructured":"Ozdemir O, Kaya AA (2019) Comparison of FCM, PCM, FPCM and PFCM algorithms in clustering methods. Afyon Kocatepe \u00dcniversitesi Fen ve M\u00fchendislik Bilimleri Dergisi 19(1):92\u2013102","journal-title":"Afyon Kocatepe \u00dcniversitesi Fen ve M\u00fchendislik Bilimleri Dergisi"},{"issue":"2","key":"734_CR24","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2):191\u2013203","journal-title":"Comput Geosci"},{"issue":"3","key":"734_CR25","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/91.531779","volume":"4","author":"R Krishnapuram","year":"1996","unstructured":"Krishnapuram R, Keller JM (1996) The possibilistic C-means algorithm: insights and recommendations. IEEE Trans Fuzzy Syst 4(3):385\u2013393","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"734_CR26","doi-asserted-by":"crossref","unstructured":"Jafar OM, Sivakumar R (2012) A study on possibilistic and fuzzy possibilistic C-means clustering algorithms for data clustering. In 2012 international conference on emerging trends in science, engineering and technology (INCOSET), pp 90\u201395, Dec 2012","DOI":"10.1109\/INCOSET.2012.6513887"},{"issue":"Supp 01","key":"734_CR27","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1142\/S0218488519400075","volume":"27","author":"L Szil\u00e1gyi","year":"2019","unstructured":"Szil\u00e1gyi L, Lefkovits S, Szil\u00e1gyi SM (2019) Self-tuning possibilistic C-means clustering models. Int J Unc Fuzz Knowl Based Syst 27(Supp 01):143\u2013159","journal-title":"Int J Unc Fuzz Knowl Based Syst"},{"issue":"4","key":"734_CR28","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TFUZZ.2004.840099","volume":"13","author":"NR Pal","year":"2005","unstructured":"Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy C-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517\u2013530","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"1","key":"734_CR29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.fss.2003.11.009","volume":"147","author":"H Timm","year":"2004","unstructured":"Timm H, Borgelt C, D\u00f6ring C, Kruse R (2004) An extension to possibilistic fuzzy cluster analysis. Fuzzy Sets Syst 147(1):3\u201316","journal-title":"Fuzzy Sets Syst"},{"key":"734_CR30","doi-asserted-by":"crossref","unstructured":"Timm H, Kruse R (2002) A modification to improve possibilistic fuzzy cluster analysis. In 2002 IEEE World congress on computational intelligence. 2002 IEEE international conference on fuzzy systems. FUZZ-IEEE\u201902. Proceedings (Cat. No.02CH37291), vol 2. Honolulu, HI, USA, pp 1460\u20131465","DOI":"10.1109\/FUZZ.2002.1006721"},{"key":"734_CR31","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neucom.2016.09.025","volume":"219","author":"S Askari","year":"2017","unstructured":"Askari S, Montazerin N, Zarandi MHF, Hakimi E (2017) Generalized entropy based possibilistic fuzzy C-means for clustering noisy data and its convergence proof. Neurocomputing 219:186\u2013202","journal-title":"Neurocomputing"},{"key":"734_CR32","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.asoc.2016.12.049","volume":"53","author":"S Askari","year":"2017","unstructured":"Askari S, Montazerin N, Fazel Zarandi MH (2017) Generalized possibilistic fuzzy C-means with novel cluster validity indices for clustering noisy data. Appl Soft Comput 53:262\u2013283","journal-title":"Appl Soft Comput"},{"key":"734_CR33","unstructured":"Unzueta D (2021) Kernel methods: a simple introduction. https:\/\/towardsdatascience.com\/kernel-methods-a-simple-introduction-4a26dcbe4ebd. Accessed 15 Oct 2021"},{"issue":"10","key":"734_CR34","doi-asserted-by":"publisher","first-page":"e0235885","DOI":"10.1371\/journal.pone.0235885","volume":"15","author":"JE Johnson","year":"2020","unstructured":"Johnson JE, Laparra V, P\u00e9rez-Suay A, Mahecha MD, Camps-Valls G (2020) Kernel methods and their derivatives: concept and perspectives for the earth system sciences. PLoS ONE 15(10):e0235885","journal-title":"PLoS ONE"},{"key":"734_CR35","unstructured":"Wu X-H, Zhou J-J (2005) Possibilistic fuzzy c-means clustering model using kernel methods. In international conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC\u201906), vol 2, pp 465\u2013470, Nov 2005"},{"key":"734_CR36","doi-asserted-by":"crossref","unstructured":"Polap D, Wo\u017aniak M (2021) Image features extractor based on hybridization of fuzzy controller and meta-heuristic. In 2021 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1\u20136, July 2021","DOI":"10.1109\/FUZZ45933.2021.9494580"},{"key":"734_CR37","first-page":"1","volume-title":"Neural nets","author":"F Camastra","year":"2006","unstructured":"Camastra F (2006) Kernel methods for clustering. In: Apolloni B, Marinaro M, Nicosia G, Tagliaferri R (eds) Neural nets. Springer, pp 1\u20139"},{"key":"734_CR38","doi-asserted-by":"publisher","first-page":"88434","DOI":"10.1109\/ACCESS.2020.2992937","volume":"8","author":"M-V Nichita","year":"2020","unstructured":"Nichita M-V, Paun M-A, Paun V-A, Paun V-P (2020) Image clustering algorithms to identify complicated cerebral diseases description and comparison. IEEE Access 8:88434\u201388442","journal-title":"IEEE Access"},{"key":"734_CR39","unstructured":"Bhatia S, Upadhyay J (2011) Image segmentation using fuzzy clustering algorithm"},{"key":"734_CR40","unstructured":"Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications-ScienceDirect. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1026309811000472"},{"key":"734_CR41","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2015\/v8i15\/73229","author":"K Sankar","year":"2015","unstructured":"Sankar K, Nirmala K (2015) Orthogonal features based classification of microcalcification in mammogram using Jacobi moments. Indian J Sci Technol. https:\/\/doi.org\/10.17485\/ijst\/2015\/v8i15\/73229","journal-title":"Indian J Sci Technol"},{"issue":"2","key":"734_CR42","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1214\/17-AOS1665","volume":"47","author":"Y Wu","year":"2019","unstructured":"Wu Y, Yang P (2019) Chebyshev polynomials, moment matching, and optimal estimation of the unseen. Ann Statist 47(2):857\u2013883","journal-title":"Ann Statist"},{"issue":"4","key":"734_CR43","first-page":"1","volume":"52","author":"P Kaur","year":"2019","unstructured":"Kaur P, Pannu HS, Malhi AK (2019) Comprehensive study of continuous orthogonal moments\u2014A systematic review. ACM Comput Surv 52(4):1\u201330","journal-title":"ACM Comput Surv"},{"key":"734_CR44","unstructured":"Discrete orthognal moment features using Chebyshev polynomials. https:\/\/www.researchgate.net\/publication\/29487108_Discrete_Orthognal_Moment_Features_Using_Chebyshev_Polynomials"},{"key":"734_CR45","doi-asserted-by":"crossref","unstructured":"Bayraktar B, Bernas T, Robinson J, Rajwa B (2006) Image reconstruction from discrete Chebyshev moments via formation of lookup tables-art. no. 61424T. Proceedings of SPIE-the international society for optical engineering, vol 6142","DOI":"10.1117\/12.651938"},{"key":"734_CR46","unstructured":"Mukundan R, Ong SH, Lee PA (2022) Discrete orthognal moment features using Chebyshev polynomials. University of Canterbury. Computer science and software engineering"},{"key":"734_CR47","unstructured":"Kernel clustering algorithm. https:\/\/www.researchgate.net\/publication\/279902909_Kernel_clustering_algorithm"},{"key":"734_CR48","unstructured":"Multiple kernel fuzzy clustering. IEEE Journals & Magazine. IEEE Xplore. https:\/\/ieeexplore.ieee.org\/document\/6031914"},{"issue":"9","key":"734_CR49","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1007\/s12524-018-0813-z","volume":"46","author":"AP Byju","year":"2018","unstructured":"Byju AP, Kumar A, Stein A, Kumar AS (2018) Combining the FCM classifier with various kernels to handle non-linearity of class boundaries. J Indian Soc Remote Sens 46(9):1519\u20131526","journal-title":"J Indian Soc Remote Sens"},{"issue":"5","key":"734_CR50","doi-asserted-by":"publisher","first-page":"052060","DOI":"10.1088\/1757-899X\/546\/5\/052060","volume":"546","author":"RA Putri","year":"2019","unstructured":"Putri RA, Rustam Z, Pandelaki J (2019) Kernel based fuzzy C-means clustering for chronic sinusitis classification. IOP Conf Ser Mater Sci Eng 546(5):052060","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"734_CR51","unstructured":"Wu Z, Xie W, Yu J (2003) Fuzzy C-means clustering algorithm based on kernel method. In: Proceedings fifth international conference on computational intelligence and multimedia applications (ICCIMA), pp 49\u201354, Sept 2003"},{"issue":"1","key":"734_CR52","doi-asserted-by":"publisher","first-page":"147","DOI":"10.15866\/irecos.v9i1.1044","volume":"9","author":"K Haridas","year":"2014","unstructured":"Haridas K, Thanamani AS (2014) An efficient image clustering and content based image retrieval using fuzzy K means clustering algorithm. Int Rev Comput Softw (IRECOS) 9(1):147","journal-title":"Int Rev Comput Softw (IRECOS)"},{"issue":"28","key":"734_CR53","first-page":"35741","volume":"80","author":"D Gangodkar","year":"2021","unstructured":"Gangodkar D (2021) A novel image retrieval technique based on semi supervised clustering. Multimed Tools Appl 80(28):35741\u201335769","journal-title":"Multimed Tools Appl"},{"key":"734_CR54","doi-asserted-by":"crossref","unstructured":"Jain M, Singh SK (2018) An efficient content based image retrieval algorithm using clustering techniques for large dataset. In 2018 4th international conference on computing communication and automation (ICCCA), pp 1\u20135, Dec 2018","DOI":"10.1109\/CCAA.2018.8777591"},{"key":"734_CR55","unstructured":"Sp\u00e5ng A (2017) Automatic image annotation by sharing labels based on image clustering"},{"key":"734_CR56","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-3-642-41062-8_7","volume-title":"Similarity search and applications","author":"U Chester","year":"2013","unstructured":"Chester U, Ratsaby J (2013) Machine learning for image classification and clustering using a universal distance measure. In: Brisaboa N, Pedreira O, Zezula P (eds) Similarity search and applications. Springer, Heidelberg, pp 59\u201372"},{"key":"734_CR57","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4018\/978-1-4666-3994-2.ch002","volume-title":"Image processing: concepts, methodologies, tools, and applications","author":"G Papakostas","year":"2013","unstructured":"Papakostas G, Karakasis EG, Koulouriotis D (2013) Orthogonal image moment invariants: highly discriminative features for pattern recognition applications. Image processing: concepts, methodologies, tools, and applications. pp 15\u201332"},{"key":"734_CR58","doi-asserted-by":"publisher","DOI":"10.1002\/9780470684757","volume-title":"Moments and moment invariants in pattern recognition","author":"J Flusser","year":"2009","unstructured":"Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley"},{"issue":"1","key":"734_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3479428","volume":"55","author":"S Qi","year":"2021","unstructured":"Qi S, Zhang Y, Wang C, Zhou J, Cao X (2021) A survey of orthogonal moments for image representation: theory, implementation, and evaluation. ACM Comput Surv 55(1):1\u201335","journal-title":"ACM Comput Surv"},{"key":"734_CR60","doi-asserted-by":"crossref","unstructured":"Yang B, Tang W, Chen X (2019) Image reconstruction by orthogonal moments derived by the parity of polynomials. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), p 1672\u20131676, May 2019","DOI":"10.1109\/ICASSP.2019.8682452"},{"issue":"2","key":"734_CR61","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1080\/23799927.2018.1457080","volume":"3","author":"P Kaur","year":"2018","unstructured":"Kaur P, Pannu HS (2018) Comprehensive review of continuous and discrete orthogonal moments in biometrics. Int J Comput Math Comput Syst Theory 3(2):64\u201391","journal-title":"Int J Comput Math Comput Syst Theory"},{"key":"734_CR62","doi-asserted-by":"crossref","unstructured":"Singh S, Urooj S (2018) Orthogonal moment extraction and classification of melanoma images","DOI":"10.20944\/preprints201803.0128.v1"},{"key":"734_CR63","unstructured":"Hunt O, Mukundan R (2004) A comparison of discrete orthogonal basis functions for image compression. https:\/\/www.semanticscholar.org\/paper\/A-Comparison-of-Discrete-Orthogonal-Basis-Functions-Hunt Mukundan\/53c069d79bba82e5ba5d4763a31f4119a8fae4bf"},{"key":"734_CR64","doi-asserted-by":"crossref","unstructured":"Hosaini SJ, Alirezaee S, Ahmadi M, Makki S (2013) Comparison of the Legendre, Zernike and pseudo-Zernike moments for feature extraction. In iris recognition, 2013 5th international conference on computational intelligence and communication networks, Sept 2013","DOI":"10.1109\/CICN.2013.54"},{"key":"734_CR65","unstructured":"Siminovitch D (2019) Chebyshev Polynomials of a discrete variable and their physical applications"},{"key":"734_CR66","unstructured":"Yap PT, Raveendran P, Ong SH (2001) Chebyshev moments as a new set of moments for image reconstruction, In IJCNN\u201901. International joint conference on neural networks. Proceedings (Cat. No.01CH37222), https:\/\/www.academia.edu\/6958689\/Chebyshev_moments_as_a_new_set_of_moments_for_image_reconstruction, Jan 2001"},{"key":"734_CR67","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-981-13-1927-3_70","volume-title":"Smart intelligent computing and applications: proceedings of the second international conference on SCI 2018","author":"K Pankaja","year":"2019","unstructured":"Pankaja K, Suma V (2019) Leaf recognition and classification using Chebyshev moments. In: Bhateja V, Das S, Satapathy SC (eds) Smart intelligent computing and applications: proceedings of the second international conference on SCI 2018. Springer, pp 667\u2013678"},{"issue":"1","key":"734_CR68","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.jmaa.2014.10.087","volume":"424","author":"P Njionou Sadjang","year":"2015","unstructured":"Njionou Sadjang P, Koepf W, Foupouagnigni M (2015) On moments of classical orthogonal polynomials. J Math Anal Appl 424(1):122\u2013151","journal-title":"J Math Anal Appl"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-022-00734-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-022-00734-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-022-00734-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T06:12:28Z","timestamp":1689055948000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-022-00734-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,3]]},"references-count":68,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["734"],"URL":"https:\/\/doi.org\/10.1007\/s12065-022-00734-x","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,3]]},"assertion":[{"value":"25 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2022","order":4,"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 the essay serves no personal or organizational interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}