Title of the article:

INDEXING OF COLOR COMBINATIONS PATTERNS OF RUSSIAN DECORATIVE AND APPLIED ART

Author(s):

Georgii I. Borzunov

Andrey V. Firsov

Alexander N. Novikov

Information about the author/authors

Georgii I. Borzunov — DSc in Technical Sciences, Professor, A. N. Kosygin Russian State University, Sadovnicheskaya St., 33, build. 1, 117997 Moscow, Russia. E-mail: borzunov_g@mail.ru

Andrey V. Firsov — DSc in Technical Sciences, Professor, A. N. Kosygin Russian State University, Sadovnicheskaya St., 33, build. 1, 117997 Moscow, Russia. E-mail: firsov_a_v@mail.ru

Alexander N. Novikov — DSc in Technical Sciences, Professor, A. N. Kosygin Russian State University, Sadovnicheskaya St., 33, build. 1, 117997 Moscow, Russia. E-mail: a_n_novikov@mail.ru 

Section

History of Arts

Year

2018

Volume

Vol. 50

Pages

pp. 284–300

Received

May 15, 2018

Date of publication

December 28, 2018

Index UDK

7.017.412 +745.04

Index BBK

85.125+85.12

Abstract

This paper is the first to perform indexing of color combinations of the spinning wheels patterns, based on a selection from the Museum Fund of A. N. Kosygin Russian State University. The authors display the efficiency of the proposed indexing method: all the images with differing color contrasts obtained different indexes, i.e. different values of characteristic vectors. The analysis of the specified characteristic vectors allowed to define features of distribution of color contrasts in indexed images and to allocate the most characteristic color combinations for patterns of spinning wheels. The computational experiment showed that this method of color image indexing can be used for automated classification of large collections of as patterns of spinning wheels so important for art studies of color images. In addition, this method of indexing color combinations can serve as a basis for content search of spinning wheels` color patterns based on the recognition of color contrasts.

Keywords

collection of images, patterns, spinning wheels, automatic classification, content search of images, control points detectors, descriptors reference points, color contrasts.

References

1 Adobe color CC. Available at: https://color.adobe.com/ru/create/color-wheel (accessed 12 November 2017). (In Russian)

2 Emotient — prilozheniia dlia Google Glass, raspoznaiushchee emotsii [Emotient — applications for Google Glass that recognize emotions]. T-human. Available at: http://t-human.com/journal/emotient-prilozheniya-dlya-google-glass-raspoznayushhee-emocii/ (accessed 12 November 2017). (In Russian)

3 Ghazi Dr., Qaryouti M. et al. A Novel Method for Color Image Recognition. International Journal of Computer Science and Mobile Computing, 2016 (November), no 11, vol. 5, pp. 57–64. (In English)

4 GNU Image Manipulation program. User manual. Gimp. Available at: https://docs.gimp.org/2.8/ru/ (accessed 12 November 2017). (In English)

5 Joutou T., Yanai K., A food image recognition system with multiple kernel learning. IEEE International Conference on Image Processing. 7–10 November 2009, pp. 285–288.
DOI: 10.1109,ICIP.2009.5413400. (In English)

6 Parker J. R. Algorithms for Image Processing and Computer Vision, Second Edition. Indianapolis, Wiley Publishing, Inc. Publ., 2012. 506 p. (In English)

7 Ableukhov S. I., Egupov R. D., Lobov, D. V. Детектирование стволов деревьев на основе алгоритмов библиотеки OpenCV [Detection of tree trunks based on the algorithms of the OpenCV library]. Innovatsionnaia nauka, 2016, no 5, pp. 9–12. (In Russian)

8 Algoritmicheskie osnovy rastrovoj mashinnoj grafiki [Algorithmic foundations of raster computer graphics], Ivanov D. V., Karpov A. S. and others. Moscow, Internet-University of Information Technologies, BINOMIAL, laboratory of knowledge Publ., 2010. 283 p. (In Russian)

9 Baygarova N. S. Bukhshtab Yu. A., Evteeva N. H. Koryagin D. A. Nekotorye podhody k organizacii soderzhatel'nogo poiska izobrazhenij i videoinformacii [Some approaches to the organization of content search of images and video data]. Sait IPM im. M. V. Keldysha [M. V. Keldysh`s IPM website]. Available at: http://www.keldysh.ru/papers/2002/prep78/prep2002_78.html (accessed 12 November 2017). (In Russian)

10 Borzunov G. I., Beschastnov N. P., Stor I. N. Indeksaciya izobrazheniya po cvetovym sochetaniyam [Indexing images by color combinations]. Dizajn i tekhnologii, 2017, no 62 (104), pp. 34–40. (In Russian)

11 Borzunov G. I., Moiseev K. A., Novikov A. N. Ispol'zovanie grafa sosedstva cvetov dlya raspoznavaniya linejnyh ehlementov v tekstil'nyh uzorah [Use of the colors` adjacency graph for recognizing of linear elements in textile pattern]. Izvestiya vysshih uchebnyh zavedenij. Tekhnologiya tekstil'noj promyshlennosti, 2012, no 2,
pp. 142–146. (In Russian)

12 Borzunov G. I., Moiseyev, A. K., Novikov A. N. Ispol'zovanie grafa sosedstva cvetov dlya raspoznavaniya cvetnyh kletok v tekstil'nyh uzorah [Use of the colors adjacency graph for recognizing colored cells in textile patterns]. Izvestiya vysshih uchebnyh zavedenij. Tekhnologiya tekstil'noj promyshlennosti, 2013, no 1, pp. 144–147. (In Russian)

13 Zakharov R. K. Metody povysheniya kachestva izobrazhenij v zadachah raspoznavaniya [Methods of image quality improvement in recognition problems]. Sovremennye nauchnye issledovaniya i innovacii, 2012, no 8. Available at: http://web.snauka.ru/issues/2012/08/16488 (accessed 29 September 2017). (In Russian)

14 Itten I. Iskusstvo cveta [The art of color], translated from Germany by L. Monahova. Leningrad, Moscow, D. Aronov Publ., 2007. 96 p. (In Russian)

15 Mironova L. N. Cvetovedenie [Color science]. Meganewton, High school Publ., 1984. 286 p. (In Russian)

16 Slepynin L. Y. Pryalka v tradicionnoj kul'ture: mifopoehtika i tipologiya (na materialah Kostromskoj gubernii) [Spinning Wheel in traditional culture: poetics and typology (based on materials of Kostroma province). Abstract of dissertation candidate of Culturology. Kirov, 2007. 18 p. (In Russian)

17 Snetkov V. Y., Sugarova L. N. Modeli i principy cvetovoj garmonii [Models and principles of color harmony]. Vestnik MEHI. Radioehlektronika, 2010, no 3,
pp. 132–135. (In Russian)

18 Hoang Ngoc Phan, Spitsyn V. G. Algoritmy dlya klassifikacii otpechatkov pal'cev na osnove primeneniya fil'tra Gabora, vejvlet-preobrazovaniya i mnogoslojnoj nejronnoj seti [Algorithms for fingerprint classification based on Gabor filter, wavelet transform and multilayer neural network application]. Izvestiya Tomskogo politekhnicheskogo universiteta, 2012, vol. 320, no 5, pp. 60–64. (In Russian)

19 Yandeks poisk kartinok i fotografij [Yandex search of pictures and photos]. Available at: http:\\images.yandex.ru (accessed 12 November 2017). (In Russian)

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