Description of images using model-oriented descriptors
Myasnikov V.V.


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

Full text of article: Russian language.


The paper proposes an approach to constructing an image description using a set of model-oriented descriptors. Each descriptor characterizes the "similarity" of the analyzed image, represented as a complex-valued gradient field, to a pre-selected model of this descriptor. It is proposed that descriptor models should be synthesized using a method of principal components, or discriminant analysis, which has been applied to a diversity of complex-valued gradient field realizations. As a result, the proposed approach enables the complex-valued field of the gradient of the analyzed image to be described as a set of real quantities from the interval [0,1], capable of simultaneously characterizing the phase and magnitude of the image gradient. The effectiveness of the proposed approach is illustrated via solving a face recognition problem and comparing the result with prototype solutions (based on the principal component method and discriminant analysis), which directly utilize halftone images. The comparison is made using a nearest neighbor's classifier.

digital images, descriptors, features, analysis, recognition, image retrieval.

Myasnikov VV. Description of images using model-oriented descriptors. Computer Optics 2017; 41(6): С. 888-896. DOI: 10.18287/2412-6179-2017-41-6-888-896.


  1. Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005; 27(10): 1615-1630. DOI: 10.1109/TPAMI.2005.188.
  2. Soyfer VA, ed. Computer image processing methods [In Russian]. Moscow: "Fizmatlit" Publisher; 2003. ISBN: 5-9221-0270-2.
  3. Duda RO, Hart PE. Pattern classification and scene analysis. New York: Wiley; 1973. 512. ISBN: 978-0471223610.
  4. Myasnikov VV. Model-based gradient field descriptor as a convenient tool for image recognition and analysis [In Russian]. Computer Optics 2012; 36(4): 596-604.
  5. Myasnikov VV. Method for detection of vehicles in digital aerial and space remote sensed images [In Russian]. Computer Optics 2012; 36(3): 429-438.
  6. Kuznetsov AV, Myasnikov VV. New algorithms for verifying the consistency between satellite images and survey conditions. Pattern Recognition and Image Analysis 2016; 26(3): 593-596. DOI: 10.1134/S1054661816030135.
  7. Dalal N, Triggs B. Histograms of oriented gradients for human detection. Proc CVPR 2005: 886-893. DOI: 10.1109/CVPR.2005.177.
  8. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comp Vision 2004; 60(2): 91-110. DOI: 10.1023/B:VISI.0000029664.99615.94.
  9. Gonzalez-Reyna SE, Avina-Cervantes JG, Ledesma-Orozco SE, Cruz-Aceves I. Eigen-gradients for traffic sign recognition. Mathematical Problems in Engineering 2013; 2013: 364305. DOI:10.1155/2013/364305.
  10. Hu R, Collomosse J. A performance evaluation of gradient field HOG descriptor for sketch based image retrieval. Computer Vision and Image Understanding 2013; 117(7): 790-806. DOI: 10.1016/j.cviu.2013.02.005.
  11. Tzimiropoulos G, Zafeiriou S, Pantic M. Principal component analysis of image gradient orientations for face recognition. IEEE Int Conf Automatic Face and Gesture Recognition (FG 2011) 2011. DOI: 10.1109/FG.2011.5771457.
  12. Khramov AG. Direction field method and its application for medicine images analysis and interpretation [In Russian]. The thesis for the Doctoral degree in Technical Sciences. Samara; 2006.
  13. Harshman RA, Lundy ME. PARAFAC: Parallel factor analysis. Computational Statistics and Data Analysis 1994; 18(1): 39-72. DOI: 10.1016/0167-9473(94)90132-5.
  14. Sommer G, ed. Geometric computing with Clifford algebras: Theoretical foundations and applications in computer vision and robotics. Berlin, Heidelberg: Springer Verlag; 2000. ISBN: 978-3-540-41198-7.
  15. Horel JD. Complex principal component analysis: Theory and examples. Journal of Climate and Applied Meteorology 1984; 23: 1660-1673. DOI: 10.1175/1520-0450(1984)023<1660:CPCATA>2.0.CO;2.
  16. Schreier PJ, Scharf LL Statistical signal processing of complex-valued data: The theory of improper and noncircular signals. Cambridge: Cambridge University Press; 2010. ISBN: 978-0-521-89772-3.
  17. Jolliffe IT. Principal component analysis. 2nd ed. New York, Berlin, Heidelberg: Springer-Verlag; 2002. ISBN: 0-387-95442-2.
  18. Brillinger DR. Time series: Data analysis and theory. Holden-Day, 1981. 540.
  19. Alfsmann D, Göckler HG, Sangwine SJ, Ell TA. Hypercomplex algebras in digital signal processing: benefits and drawbacks. EUSIPCO 2007: 1322-1326.
  20. Delac K, Grgic M, Grgic S. Independent comparative study of PCA, ICA, and LDA on the FERET data set. Int J Imaging Syst Technol 2005; 15(5): 252-260. DOI: 10.1002/ima.20059.
  21. Duin RPW, de Ridder D, Tax DMJ. Featureless pattern classification. Kybernetika 1998; 34(4): 399-404.
  22. Advances in face image analysis: Techniques and technologies. Ed by Zhang YJ. IGI Global, USA 2011. 350.
  23. Georghiades AS, Belhumeur PN, Kriegman DJ. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001; 23(6): 643-660. DOI: 10.1109/34.927464.
  24. Lee KC, Ho J, Kriegman D. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005; 27(5): 684-698. DOI: 10.1109/TPAMI.2005.92.
  25. Turk M, Pentland A. Eigenfaces for recognition. J Cogn Neurosci 1991; 3(1): 71-86. DOI: 10.1162/jocn.1991.3.1.71.

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