Development of algorithm for automatic construction of a computational procedure of local image processing, based on the hierarchical regression
V.N. Kopenkov, V.V. Myasnikov

 

Image Processing Systems Institute оf RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russia,
Samara National Research University, Samara, Russia

Full text of article: English language.

 PDF

Abstract:

In this paper, we propose an algorithm for the automatic construction (design) of a computational procedure for non-linear local processing of digital signals/images. The aim of this research is to work out an image processing algorithm with a predetermined computational complexity and achieve the best quality of processing on the existing data set, while avoiding a problem of retraining or doing with less training. To achieve this aim we use a local discrete wavelet transform for a preliminary image analysis and the hierarchical regression to construct a local image processing procedure on the basis of a training dataset. Moreover, we work out a method to decide whether the training process should be completed or continued. This method is based on the functional of full cross-validation control, which allows us to construct the processing procedure with a predetermined computational complexity and veracity, and with the best quality.

Keywords:
local processing, hierarchical regression, computational efficiency, machine learning, precedent-based processing, functional of full cross-validation.

Citation:
Kopenkov VN, Myasnikov VV. Development of an algorithm for automatic construction of a computational procedure of local image processing, based on the hierarchical regression. Computer Optics 2016; 40(5): 713-720. DOI: 10.18287/2412-6179-2016-40-5-713-720.

References:

  1. Soifer VA, ed, Chernov AV, Chernov VM, Chicheva MA, Fursov VA, Gashnikov MV, Glumov NI, Ilyasova NY, Khramov AG, Korepanov AO, Kupriyanov AV, Myasnikov EV, Myasnikov VV, Popov SB, Sergeyev VV. Computer Image Processing, Part II: Methods and algorithms. VDM Verlag; 2010. ISBN: 978-3-639-17545-5.
  2. Woods R, Gonzalez R. Digital Image Processing. 2nd ed. Prentice Hall; 2002. ISBN 0-201-18075-8.
  3. Pratt W. Digital image processing. 4th ed. Wiley-Interscience; 2007. ISBN: 978-0-471-76777-0.
  4. Haykin S. Neural Networks: A Comprehensive Foundation. Upper Saddle River, NJ: Prentice Hall; 1999. ISBN 0-13-273350-1.
  5. Breiman L Friedman J, Olshen R, Stone C. Classification and regression trees. Monterey, CA: Wadsworth, Inc.; 1984. ISBN 978-0412048418.
  6. Kopenkov V, Myasnikov V. An algorithm for automatic construction of computational procedure of non-linear local image processing on the base of hierarchical regression [In Russian]. Computer optics 2012; 36(2):257-266.
  7. Vapnik, VN, Chervonenkis AYa. Theory of Pattern Recognition [in Russian]. Moscow: “Nauka” Publisher; 1974.
  8. Vorontsov K. A combinatorial approach to assessing the quality of training algorithm [In Russian]. Mathematical problems of cybernetics 2004; 13: 5-36.
  9. Kopenkov V. Efficient algorithms of local discrete wavelet transform with HAAR-like bases. Pattern Recognition and Image Analysis 2008; 18(4): 654-661. DOI: 10.1134/S1054661808040184.
  10. Kopenkov V. On halting the process of hierarchical regression construction when implementing computational procedures for local image processing. Pattern Recognition and Image Analysis 2014; 24(4): 506-510. DOI:  10.1134/S1054661814040087.
© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; E-mail: journal@computeroptics.ru; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20