• Mustafa Özendi
  • Alper Yılmaz
Keywords: edge detection, conic sections, projective invariants, pattern recognition


Classification of image groups has different applications in remote sensing, photogrammetry, digital painting catalogues and security related areas. Researchers from remote sensing, computer vision and photogrammetry have used different approaches based on image features (color, textures, object shapes) in order to develop robust classification methods. In this study, classification of viewpoint independent image groups is developed based on the principle of invariant properties of conic sections under projective transformation.

Since invariant signatures are high dimensional data, Support Vector Machines which is a stochastic pattern recognition algorithm is used instead of classical deterministic methods. Performance of classification is evaluated using ROC (Receiver Operating Characteristics) analysis.At the beginning of this study a data set is created for testing the method. This dataset consists of nine categories of images, for each category ten images used that are taken from different viewpoints. Edge detection is applied on each image to detect boundary of objects in images. Detected edges are used for conic fitting so that each conic will be represented as a set of conic sections. Under projective transformation conic sections remain as conic section even if their shapes change. For each image in the dataset an invariant signature is computed using set of conic sections. It is assumed that there is similarity between invariant signatures of images belong to the same image category. These invariant signatures are used in histogram form for visual representation and computations.


[1] Rui, Y., Huang, S., Chang, S.F., (1999), “Image Retrieval: Current Techniques, Promising Directions, and Open Issues”, Journal of Visual Communication and Image Representation, Vol.10, No.1 ,pp.39-62.
[2] Stricker, M., Orengo, M., (1995), “Similarity of Color Images”, Storage and Retrieval for Image and Video Databases (SPIE)'95Konferansı, CA, ABD,pp.381-392.
[3] Swain, M.J., Ballard, D.H., (1991), “Color Indexing”, International Journal of Computer Vision, Vol. 7 No. 1 pp. 11-32.
[4] Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Petkovic, D., Yanker, P., Faloutsos, C., Taubin, G., (1993) “The QBIC Project: Querying Images by Content, Using Color, Texture, and Shape”, Storage and Retrieval for Image and Video Database (SPIE), Vol. 1908, pp. 173-187.
[5] Smith, J.R., Chang, S.F., (1996), “Tools and Techniques for Color Image Retrieval”, IS&T/SPIE, Vol.2670, pp. 426-437.
[6] Deng, Y., Manjunath, B. S., Kenney, C., Moore, M. S., Shin, H., (2001), “An Efficient Color Representation for Image Retrieval”, IEEE Transactions on Image Processing, Vol. 10, No. 1, pp. 140-147.
[7] Ma, W. Y., Manjunath, B. S., (1997), “Edge Flow: A Framework for Boundary Detection and Image Segmentation”, IEEE Conference on Computer Vision and Pattern Recognition, pp.744- 749.
[8] GimelFarb, G. L., Jain, A. K., (1996), “On Retrieving Textured Images From an Image Database”, Pattern Recognition, Vol. 29, No.9, pp. 1461-1483.
[9] Carter, P. H., (1991), “Texture Discrimination Using Wavelets”, SPIE Applications of Digital Image Processing XIV, Vol. 1567,pp. 432-438.
[10] Manjunath, B. S., Ma, W. Y., (1996), “Texture Features for Browsing and Retrieval of Image Data, IEEE Transactions on Pattern Analysis and Machine Intelligence”, Vol. 18, No. 8,pp. 837-842.
[11] Choi, H., Baraniuk, R. G., (1999), “Multiscale Image Segmentation Using Wavelet-Domain Hidden Markov Models”, IEEE Transactions on Image Processing,Vol. 10, pp. 1309-1321.
[12] Do, M. N., Vetterli, M., (2002), ‘Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance”, IEEE Transactions on Image Processing, Vol. 11, No.2, pp. 146-158.
[13] Li, C. S., Castelli, V., (1997), “Deriving Texture Feature Set for Content – Based Retrieval of Satellite Image Database’, ICIP 97 Konferansı, Washington DC, ABD , pp: 576-579.
[14] Randen, T., Husoy, J. H., (1999), “Filtering for Texture Classification: A Comparative Study, IEEE Transactions on Pattern Analysis and Machine Intelligence”, Vol. 21, No.4, pp. 291-310.
[15] Zhang, D., Wong, A., Indrawan, M., Lu, G., (2000), “Content Based Image Retrieval Using Gabor Texture Features”, IEEE Transactions PAMI, pp.13-15.
[16] Fu, X., Li, Y., Harrison, R., Belkasim, S., (2006), “Content Based Image Retrieval Using Gabor-Zernike Features”, ICPR 06 Konferansı, Hong Kong, pp. 417-420.
[17] Datta, R., (2008), “Image Retrieval: Ideas, Influences, and Trends of the New Age”, ACM Computing Surveys,Vol.40, No.2, pp.1-60.
[18] Zahn, C. T., Roskies, R. Z., (1972), “Fourier Descriptors for Plane Closed Curves”, IEEE Transactions on Computer, Vol. 21, No. 3, pp. 269-281.
[19] Persoon, E., Fu, K. S., (1977), “Shape Discrimination Using Fourier Descriptors”, IEEE Transactions on Systems Man and Cybernetics, SMC- Vol. 7, No.3, pp. 170-179.
[20] Hu, M. K., (1962), “Visual Pattern Recognition by Moment Invarıants, Computer Methods in Image Analysis”, IRE Transactions on Information Theory,Vol. 8.
[21] Yang, L., Albregtsen, F., (1994), “Fast Computation of Invariant Geometric Moments: A New Method Giving Correct Results”,12th IAPR International Conference on Computer Vision & Image Processing, Kudüs, İsrail, pp. 201-204.
[22] Mundy, J. L., Zisserman, A., (1992), “Geometric Invariance in Computer Vision”, (Cambridge, MA, USA: MIT Press).
[23] Srestasathiern, P., (2008), “View Invariant Planar – Object Recognition”, Yüksek Lisans Tezi, The Ohio State University.
[24] Maini, R., Aggarwal, H., (2009), “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing, 3(1),1-12.
[25] ParisS,2009, http//, (13.09.2012).
[26], (1.10.2012).
How to Cite
M. Özendi and A. Yılmaz, “CLASSIFICATION OF VIEWPOINT INDEPENDENT IMAGE GROUPS”, JAST, vol. 6, no. 1, pp. 87-94, Jan. 2013.