Could k-NN classifier be Useful in tree leaves recognition?

• Autorzy: Horaisová K.

Identyfikatory

DOI

10.2478/acsc-2014-0011

• Języki publikacji: EN

Abstrakty

EN

This paper presents a method for affine invariant recognition of two-dimensional binary objects based on 2D Fourier power spectrum. Such function is translation invariant and their moments of second order enable construction of affine invariant spectrum except of the rotation effect. Harmonic analysis of samples on circular paths generates Fourier coefficients whose absolute values are affine invariant descriptors. Affine invariancy is approximately saved also for large digital binary images as demonstrated in the experimental part. The proposed method is tested on artificial data set first and consequently on a large set of 2D binary digital images of tree leaves. High dimensionality of feature vectors is reduced via the kernel PCA technique with Gaussian kernel and the k-NN classifier is used for image classification. The results are summarized as k-NN classifier sensitivity after dimensionality reduction. The resulting descriptors after dimensionality reduction are able to distinguish real contours of tree leaves with acceptable classification error. The general methodology is directly applicable to any set of large binary images. All calculations were performed in the MATLAB environment.

Słowa kluczowe

EN

binary image; Fourier transform; affine invariance; harmonic analysis; pattern recognition; k-NN classifier

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2014
• Tom: Vol. 24, no. 2
• Strony: 177--192
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Horaisová K.

• Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering, Trojanova 13, Prague, Czech Republic

Bibliografia

• [1] D. G. Altman and J. M. Bland: Statistics Notes: Diagnostic tests 1: sensitivity and specificity. British Medical J., 308(6943), (1994), 1552.
• [2] S. Antani, L. R. Long and G. R. Thoma: A biomedical information system for combined content-based retrieval of spine X-ray images and associated text information. Proc. of the Indian Conf. on Computer Vision, Graphics, and Image Processing, (2002), 16-21.
• [3] S. Antani, L. R. Long and G. R. Thoma: Content-based image retrieval for large biomedical image archives. Proc. of 11th World Congress on Medical Infor matics, (2004), 829-833.
• [4] G. Bordogna, L. Ghilardi, S. Milesi and M. Pagani: A Flexible System for the Retrieval of Shapes in Binary Images. Applications of Fuzzy Sets Theory, Lecture Notes in Computer Science, 4578 (2007), 370-377.
• [5] R. Datta, D. Joshi, J. Li and J.Z. Wang: Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, 40 (2008), 1-60.
• [6] R. O. Duda, P. E. Hart and D. G. Stork: Pattern Classification. Wiley Interscience, 2000.
• [7] J.-X. Du, X.-F. Wang and G.-J. Zhang: Leaf shape based plant species recognition. Applied Mathematics and Computation, 185 (2007), 883-893.
• [8] P. Dukkipati and L. Brown: Improving the recognition of geometrical shapes in road signs by augmenting the database. Proc. of the Third Int. Conf. on Computer Science and its Applications, (2005), 8-13.
• [9] J. Flusser and T. Suk: Pattern recognition by affine moment invariants. Pattern Recognition, 26(1), (1993), 167-174.
• [10] R. Gonzales and R. Woods: Digital Image Processing. Prentice-Hall, 2001.
• [11] J. Ho and M. Yang: On affine registration of planar point sets using complex numbers. Computer Vision and Image Understanding, 115(1), (2011), 50-58.
• [12] W.-Y. Kim and Y.-S. Kim: A region-based shape descriptor using Zernike moments. Signal Processing: Image Communication, 16 (2000), 95-102.
• [13] F. Long, H. J. Zhang and D. D. Feng: Fundamentals of content-based image retrieval. Multimedia Information Retrieval and Management - Technological Fundamentals and Applications, (2003), 1-26.
• [14] P. Novotny and T. Suk: Leaf recognition of woody species in Central Europe. Biosystems Engineering, 115(4), (2013), 444-452.
• [15] A. F. Sheta, A. Baareh andM. Al-Batah: 3D Object Recognition Using Fuzzy Mathematical Modeling of 2D Images. Int. Conf. On Multimedia Computing And Systems, (2012), 278-283.
• [16] A. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain: Contentbased image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22 (2000), 1349-1380.
• [17] T. Suk and J. Flusser: Affine moment invariants generated by graph method. Pattern Recognition, 44(9), (2011), 2047-2056.
• [18] P. Sidiropoulos, S. Vrochidis and I. Kompatsiaris: Content-based binary image retrieval using the adaptive hierarchical density histogram. Pattern Recognition, 44 (2011), 739-750.
• [19] R. Veltkamp, H. Burkhardt and H.-P. Kriegel: State-of-the-Art in Content-Based Image and Video Retrieval. Kluwer Academic Publishers, 2008.
• [20] Z. Wang, Z. Chi and D. Feng: Shape based leaf image retrieval. IEE Proc. on Vision, Image, and Signal Processing, 150 (2003), 34-43.
• [21] I. Yahiaoui, N. Herve and N. Boujemaa: Shape-based image retrieval in botanical collections. Proc. of IEEE Pacific Rim Conf. on Multimedia, (2006), 357-364.
• [22] J. Yang, R. Lan; Y. Y. Tang and Y. Chen: Radial centroid curve for affine invariant features extraction. Int. J. of Wavelets Multiresolution and Information Processing, 10(4), (2012), Art. No. 1250035.
• [23] J. Yang, Y. Chen and M. Scalia: Construction of affine invariant functions in spatial domain. Mathematical Problems in Engineering, (2012), Art. No. 690262.
• [24] J. Yang, M. Li, Z. Chen and Y. Chen: Cutting affine moment invariants. Mathematical Problems in Engineering, (2012), Art. No. 928161.
• [25] Shape data set for the MPEG-7 core experiment CE-Shape-1: http://www.cis.temple.edu/∼latecki/TestData/ mpeg7shapeB.tar.gz