A New Minimum Spanning Tree-Based Method for Shape Description and Matching Working in Discrete Cosine Space

In this article, a new minimum spanning tree-based method for shape description and matching is proposed. Its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy objects was studied in the context of small and low resolution binary images. The approach seems to have many desirable properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing, an alternative solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise, with an acceptable time computing. The position with a reference approach like Zernike moments is also investigated to measure the relevance of the proposed technique.

[1]  Y Xu,et al.  Minimum spanning trees for gene expression data clustering. , 2001, Genome informatics. International Conference on Genome Informatics.

[2]  Alfred O. Hero,et al.  Robust entropy estimation strategies based on edge weighted random graphs , 1998, Optics & Photonics.

[3]  Chun-Shin Lin,et al.  New forms of shape invariants from elliptic fourier descriptors , 1987, Pattern Recognit..

[4]  Ronald L. Graham,et al.  On the History of the Minimum Spanning Tree Problem , 1985, Annals of the History of Computing.

[5]  Soo-Chang Pei,et al.  Normalization of rotationally symmetric shapes for pattern recognition , 1992, Pattern Recognit..

[6]  Young Kee Ryu,et al.  Study on the center of rotation method based on minimum spanning tree matching algorithm for fingerprint recognition , 2004 .

[7]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[8]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..

[9]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[10]  J. Yukich,et al.  Limit Theorems and Rates of Convergence for Euclidean Functionals , 1994 .

[11]  Bart Lamiroy,et al.  Graphics recognition - from re-engineering to retrieval , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[12]  P. Yip,et al.  Discrete Cosine Transform: Algorithms, Advantages, Applications , 1990 .

[13]  A. Rényi.  On Measures of Entropy and Information , 1961 .

[14]  Laurent Wendling,et al.  Indexing of technical line drawings based on F-signatures , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[15]  N. Kita.  Object locating based on concentric circular description , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[16]  Godfried T. Toussaint,et al.  The relative neighbourhood graph of a finite planar set , 1980, Pattern Recognit..

[17]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[18]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[19]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Alfred O. Hero,et al.  Robust estimation of point process intensity features using k-minimal spanning trees , 1997, Proceedings of IEEE International Symposium on Information Theory.

[21]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Ernest Valveny,et al.  A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documents , 2003, PRIS.

[23]  Majid Ahmadi,et al.  Efficient algorithm for fast computation of Zernike moments , 1996 .

[24]  R. Ravi,et al.  Spanning trees short or small , 1994, SODA '94.

[25]  Yves Lecourtier,et al.  Combination of Invariant Pattern Recognition Primitives on Technical Documents , 1999, GREC.

[26]  William K. Pratt,et al.  Scene Adaptive Coder , 1984, IEEE Trans. Commun..

[27]  Petros Maragos,et al.  Morphological skeleton representation and coding of binary images , 1984, IEEE Trans. Acoust. Speech Signal Process..

[28]  David Malah,et al.  Morphological reduction of skeleton redundancy , 1994, Signal Process..

[29]  Petros Maragos,et al.  Morphological skeleton representation and coding of binary images , 1984, IEEE Trans. Acoust. Speech Signal Process..

[30]  Alireza Khotanzad,et al.  Rotation invariant image recognition using features selected via a systematic method , 1990, Pattern Recognit..

[31]  Alfred O. Hero,et al.  Asymptotic theory of greedy approximations to minimal k-point random graphs , 1999, IEEE Trans. Inf. Theory.

[32]  Michael A. Soss.  The Size of the Open Sphere of Influence Graph in Linfty Metric Spaces , 1998, GD.

[33]  Stanislaw Osowski,et al.  Fourier and wavelet descriptors for shape recognition using neural networks - a comparative study , 2002, Pattern Recognit..

[34]  Hélène Laurent,et al.  Object Recognition Using Local Characterisation and Zernike Moments , 2005, ACIVS.

[35]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Thomas H. Reiss,et al.  Recognizing Planar Objects Using Invariant Image Features , 1993, Lecture Notes in Computer Science.

[37]  Philip N. Klein,et al.  A randomized linear-time algorithm to find minimum spanning trees , 1995, JACM.

[38]  Yazid M. Sharaiha,et al.  A minimum spanning tree approach to line image analysis , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[39]  A. Haar.  Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[40]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[41]  Chee-Way Chong,et al.  A comparative analysis of algorithms for fast computation of Zernike moments , 2003, Pattern Recognit..

[42]  Werner Nagel.  Image Analysis and Mathematical Morphology. Volume 2: Theoretical Advances. Edited by Jean Serra , 1870 .

[43]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[44]  Alfred O. Hero,et al.  Image registration with minimum spanning tree algorithm , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[45]  Laurent Wendling,et al.  Recognition of symbols in grey level line-drawings from an adaptation of the Radon transform , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[46]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[47]  G.S. Moschytz,et al.  Practical fast 1-D DCT algorithms with 11 multiplications , 1989, International Conference on Acoustics, Speech, and Signal Processing,.