nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

A Novel Algorithm to Classify Hand Drawn Sketches with Respect to Content Quality

verfasst von : Ochilbek Rakhmanov

Erschienen in: Computational Science and Its Applications – ICCSA 2020

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, the methodology of a novel algorithm called Counting Key-Points algorithm (CKP) is presented. The algorithm can be used during classification of same type of hand drawn sketches, where content quality is important. In brief, the algorithm uses reference pictures set to form vocabulary of key points (with descriptors) and counting how many times those key points appeared on other images, to decide the image content quality. CKP was tested on Draw-a-Person test images, drawn by primary school students, and reached 65% of classification accuracy. The results of the experiment show that the method is applicable and can be improved with further researches. The classification accuracy of CKP was compared to other state-of-art hand drawn image classification methods, to show superiority of the algorithm. As the dataset needs further studies to improve the prediction accuracy, it would be released to the community.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Detection of Thyroid Nodules Through Neural Networks and Processing of Echographic Images

Nächstes Kapitel Using Market Basket Analysis to Find Semantic Duplicates in Ontology

Ha, D., Eck, D.: A neural representation of sketch drawings (2017). arXiv preprint arXiv:1704.03477

Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM Trans. Graph. 31(4), 1–10 (2012)

Schneider, R.G., Tuytelaars, T.: Sketch classification and classification-driven analysis using fisher vectors. ACM Trans. Graph. (TOG) 33, 1–9 (2014)CrossRef

Yu, Q., Yang, Y., Liu, F., Song, Y.-Z., Xiang, T., Hospedales, T.M.: Sketch-a-Net: a deep neural network that beats humans. Int. J. Comput. Vis. 122, 411–425 (2017). https://doi.org/10.1007/s11263-016-0932-3MathSciNetCrossRef

Rakhmanov, O.: Testing strength of the state-of-art image classification methods for hand drawn sketches. In: 2019 15th International Conference on Electronics, Computer and Computation (ICECCO), pp. 1–3. IEEE (2019)

Jolley, R.P.: Children and Pictures: Drawing and Understanding. Wiley (2009)

Goodenough, F.L.: Measurement of intelligence by drawings (1926)

Rey, A.: L’examen psychologique dans les cas d’encephalopathie traumatique. Arch. Psychol. 28, 112 (1942)

Wolf-Klein, G.P., Silverstone, F.A., Levy, A.P., Brod, M.S., Breuer, J.: Screening for Alzheimer’s disease by clock drawing. J. Am. Geriatr. Soc. 37, 730–734 (1989)CrossRef

10.

Fastenau, P.S., Denburg, N.L., Hufford, B.J.: Adult norms for the Rey-Osterrieth Complex Figure Test and for supplemental recognition and matching trials from the Extended Complex Figure Test. Clin. Neuropsychologist 13, 30–47 (1999)CrossRef

11.

Naglieri, J.A., MacNeish, T.J., Bardos, A.: Draw a person: screening procedure for emotional disturbance. dap: sped. Pro-Ed (1991)

12.

Sun, Z., Wang, C., Zhang, L., Zhang, L.: Query-adaptive shape topic mining for hand-drawn sketch recognition. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 519–528 (2012)

13.

Rakhmanov, O., Dane, S.: Correlation between vanderbilt ADHD diagnostic scale and the draw-a-man test in school children. J. Res. Med. Dent. Sci. 7, 77–81 (2019)

14.

Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7CrossRefMATH

15.

Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd (2017)

16.

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRef

17.

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) 2005 Jun 20, vol. 1, pp. 886–893. IEEE (2005)

18.

Yang, J., Jiang, Y.-G., Hauptmann, A.G., Ngo, C.-W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, pp. 197–206. ACM, New York, NY, USA (2007). https://doi.org/10.1145/1290082.1290111

19.

Baxes, G.A.: Digital Image Processing: Principles and Applications. Wiley, New York (1994)

20.

Li, Y., Hospedales, T.M., Song, Y.-Z., Gong, S.: Free-hand sketch recognition by multi-kernel feature learning. Comput. Vis. Image Underst. 137, 1–11 (2015)CrossRef

21.

Yu, Q., Yang, Y., Song, Y.-Z., Xiang, T., Hospedales, T.: Sketch-a-Net that Beats Humans (2015). arXiv:1501.07873 [cs]

22.

Ballester, P., Araujo, R.M.: On the performance of GoogLeNet and AlexNet applied to sketches. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)

23.

Xu, P., Joshi, C.K., Bresson, X.: Multi-graph transformer for free-hand sketch recognition (2019). arXiv preprint arXiv:1912.11258

24.

Bensalah, A., Riba, P., Fornés, A., Lladós, J.: Shoot less and sketch more: an efficient sketch classification via joining graph neural networks and few-shot learning. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), pp. 80–85. IEEE (2019)

25.

Yama, M.F.: The usefulness of human figure drawings as an index of overall adjustment. J. Pers. Assess. 54, 78–86 (1990)CrossRef

26.

Naglieri, J.A., McNeish, T.J., Achilles, N.: Draw a person test. In: Tools of the Trade: A Therapist’s Guide to Art Therapy Assessments, p. 124 (2004)

27.

Rakhmanov, O., Agwu, N.N., Adeshina, S.A.: Experimentation on hand drawn sketches by children to classify Draw-a-Person test images in psychology. In: The Thirty-Third International Flairs Conference (2020)

28.

Tan, P.-N.: Introduction to Data Mining. Pearson Education, India (2018)

29.

Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: An efficient alternative to SIFT or SURF. In: ICCV, p. 2. Citeseer (2011)

30.

Gary, B., Adrian, K.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Inc. (2008)

31.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH

Titel: A Novel Algorithm to Classify Hand Drawn Sketches with Respect to Content Quality
verfasst von: Ochilbek Rakhmanov
Verlag: Springer International Publishing
Buch: Computational Science and Its Applications – ICCSA 2020
Print ISBN: 978-3-030-58810-6

Electronic ISBN: 978-3-030-58811-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-58811-3_13

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner