Shape classification using line segment statistics

In this study, we use straight-line segment statistics for shape analysis.For each contour point, we consider a continuous portion with length equal to a pre-defined percentage of the contour size.Then, we compute the length of the straight-line segment between its extreme points.For the set of straight-line segments, we compute statistical moments (average and stardard deviation). Contour shape description is an important field in computer vision. This is due to the fact that shape is an important low level image feature. In light of this, many approaches have been proposed in order to analyze it. Therefore, this paper introduces a very simple, yet efficient, shape descriptor based on straight-line segment statistics. For each contour point, we consider a continuous portion of the contour with length equal to a pre-defined percentage of the contour size. Then, we compute the length of the straight-line segment between its extreme points. For the set of straight-line segments, we compute statistical moments (average and standard deviation). Lastly, we perform this calculation for different lengths of contour portions. The proposed shape descriptor is a powerful tool for shape discrimination: it is robust (it can characterize a huge set of different classes of shapes) and is tolerant to variations in the shapes' scale and orientation. Classification results of the proposed method overcome traditional methods found in literature, proving that it is an efficient tool for shape analysis.

[1]  Luciano da Fontoura Costa,et al.  A graph-based approach for multiscale shape analysis , 2004, Pattern Recognit..

[2]  M. Zhenjiang.  Zernike moment-based image shape analysis and its application , 2000 .

[3]  Mohamed Daoudi,et al.  A Bayesian 3-D Search Engine Using Adaptive Views Clustering , 2007, IEEE Transactions on Multimedia.

[4]  O. Bruno,et al.  Leaf shape analysis using the multiscale Minkowski fractal dimension, a new morphometric method: a study with Passiflora (Passifloraceae) , 2005 .

[5]  Zhuowen Tu,et al.  Improving Shape Retrieval by Learning Graph Transduction , 2008, ECCV.

[6]  Wen-Yen Wu,et al.  Detecting the Dominant Points by the Curvature-Based Polygonal Approximation , 1993, CVGIP Graph. Model. Image Process..

[7]  B. Everitt,et al.  Applied Multivariate Data Analysis: Everitt/Applied Multivariate Data Analysis , 2001 .

[8]  Saeid Belkasim,et al.  Invariant curvature-based Fourier shape descriptors , 2012, J. Vis. Commun. Image Represent..

[9]  Wu-Chih Hu,et al.  A rotationally invariant two-phase scheme for corner detection , 1996, Pattern Recognit..

[10]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yue Gao,et al.  Camera Constraint-Free View-Based 3-D Object Retrieval , 2012, IEEE Transactions on Image Processing.

[12]  Remco C. Veltkamp,et al.  Properties and Performance of Shape Similarity Measures , 2006, Data Science and Classification.

[13]  Wei Xu,et al.  Identification of multiple damage in beams based on robust curvature mode shapes , 2014 .

[14]  Benjamin B. Kimia,et al.  Symmetry-Based Indexing of Image Databases , 1998, J. Vis. Commun. Image Represent..

[15]  Yue Gao,et al.  3-D Object Retrieval and Recognition With Hypergraph Analysis , 2012, IEEE Transactions on Image Processing.

[16]  J. Overall,et al.  Applied multivariate analysis , 1983 .

[17]  Miroslaw Bober,et al.  Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization , 2011, Computational Imaging and Vision.

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

[19]  Laurent D. Cohen,et al.  Matching 2D and 3D articulated shapes using the eccentricity transform , 2011, Comput. Vis. Image Underst..

[20]  Longin Jan Latecki,et al.  Path Similarity Skeleton Graph Matching , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Mohan S. Kankanhalli,et al.  Shape Measures for Content Based Image Retrieval: A Comparison , 1997, Inf. Process. Manag..

[22]  Philip N. Klein,et al.  Recognition of shapes by editing their shock graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Luciano da Fontoura Costa,et al.  Shape Analysis and Classification: Theory and Practice , 2000 .

[24]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[25]  Longin Jan Latecki,et al.  Skeleton-Based Shape Classification Using Path Similarity , 2008, Int. J. Pattern Recognit. Artif. Intell..

[26]  P. Wintz,et al.  An efficient three-dimensional aircraft recognition algorithm using normalized fourier descriptors , 1980 .

[27]  André Ricardo Backes,et al.  Shape Skeleton Classification Using Graph and Multi-scale Fractal Dimension , 2010, ICISP.

[28]  Wenyu Liu,et al.  Bag of contour fragments for robust shape classification , 2014, Pattern Recognit..

[29]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[30]  Deniz Erdogmus,et al.  Contour-based shape representation using principal curves , 2013, Pattern Recognit..

[31]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

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