Transportation Distances on the Circle

This paper is devoted to the study of the Monge-Kantorovich theory of optimal mass transport, in the special case of one-dimensional and circular distributions. More precisely, we study the Monge-Kantorovich problem between discrete distributions on the unit circle S1, in the case where the ground distance between two points x and y is defined as h(d(x,y)), where d is the geodesic distance on the circle and h a convex and increasing function. This study complements previous results in the literature, holding only for a ground distance equal to the geodesic distance d. We first prove that computing a Monge-Kantorovich distance between two given sets of pairwise different points boils down to cut the circle at a well chosen point and to compute the same distance on the real line. This result is then used to obtain a dissimilarity measure between 1-D and circular discrete histograms. In a last part, a study is conducted to compare the advantages and drawbacks of transportation distances relying on convex or concave cost functions, and of the classical L1 distance. Simple retrieval experiments based on the hue component of color images are shown to illustrate the interest of circular distances. The framework is eventually applied to the problem of color transfer between images.

[1]  W. Gangbo,et al.  The geometry of optimal transportation , 1996 .

[2]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[3]  Ján Morovic,et al.  Accurate 3D image colour histogram transformation , 2003, Pattern Recognit. Lett..

[4]  H. Greenspan,et al.  Region correspondence for image matching via EMD flow , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[5]  R. McCann Existence and uniqueness of monotone measure-preserving maps , 1995 .

[6]  Sung-Hyuk Cha,et al.  On measuring the distance between histograms , 2002, Pattern Recognit..

[7]  C. Gurwitz Weighted median algorithms for L1 approximation , 1990 .

[8]  Helen C. Shen,et al.  Generalized texture representation and metric , 1983, Comput. Vis. Graph. Image Process..

[9]  Julien Rabin,et al.  Regularization of transportation maps for color and contrast transfer , 2010, 2010 IEEE International Conference on Image Processing.

[10]  Mauro Dell'Amico,et al.  Assignment Problems , 1998, IFIP Congress: Fundamentals - Foundations of Computer Science.

[11]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[12]  Michael Werman,et al.  A Linear Time Histogram Metric for Improved SIFT Matching , 2008, ECCV.

[13]  C. Villani Topics in Optimal Transportation , 2003 .

[14]  C. Villani Optimal Transport: Old and New , 2008 .

[15]  Haibin Ling,et al.  An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Trevor Darrell,et al.  Fast contour matching using approximate earth mover's distance , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[17]  U. Frisch,et al.  A reconstruction of the initial conditions of the Universe by optimal mass transportation , 2001, Nature.

[18]  Carlo Tomasi,et al.  Edge, Junction, and Corner Detection Using Color Distributions , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Lei Zhu,et al.  Optimal Mass Transport for Registration and Warping , 2004, International Journal of Computer Vision.

[20]  Yann Gousseau,et al.  Adaptive image retrieval based on the spatial organization of colors , 2008, Comput. Vis. Image Underst..

[21]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[22]  Farzin Mokhtarian,et al.  Silhouette-based occluded object recognition through curvature scale space , 1997, Machine Vision and Applications.

[23]  David W. Jacobs,et al.  Approximate earth mover’s distance in linear time , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Lei Zhu,et al.  An Image Morphing Technique Based on Optimal Mass Preserving Mapping , 2007, IEEE Transactions on Image Processing.

[25]  Hayit Greenspan,et al.  Context-based image modelling , 2002, Object recognition supported by user interaction for service robots.

[26]  Julien Rabin,et al.  Circular Earth Mover’s Distance for the comparison of local features , 2008, 2008 19th International Conference on Pattern Recognition.

[27]  R. McCann Exact solutions to the transportation problem on the line , 1999, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[28]  Wen Gao,et al.  Effective and efficient object-based image retrieval using visual phrases , 2006, MM '06.

[29]  Carlos Cabrelli,et al.  A Linear Time Algorithm for a Matching Problem on the Circle , 1998, Inf. Process. Lett..

[30]  Kai Li,et al.  Image similarity search with compact data structures , 2004, CIKM '04.

[31]  Mauro Dell'Amico,et al.  8. Quadratic Assignment Problems: Algorithms , 2009 .

[32]  Michael Werman,et al.  Fast and robust Earth Mover's Distances , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Ying Liu,et al.  Region-Based Image Retrieval with High-Level Semantic Color Names , 2005, 11th International Multimedia Modelling Conference.

[34]  Michael Werman,et al.  Bipartite Graph Matching for Points on a Line or a Circle , 1986, J. Algorithms.

[35]  Arthur Cayley,et al.  The Collected Mathematical Papers: On Monge's “Mémoire sur la théorie des déblais et des remblais” , 2009 .

[36]  Carlos Cabrelli,et al.  The Kantorovich metric for probability measures on the circle , 1995 .

[37]  Julien Rabin,et al.  A Statistical Approach to the Matching of Local Features , 2009, SIAM J. Imaging Sci..

[38]  Julien Rabin,et al.  Geodesic Shape Retrieval via Optimal Mass Transport , 2010, ECCV.

[39]  Azriel Rosenfeld,et al.  A distance metric for multidimensional histograms , 1985, Comput. Vis. Graph. Image Process..

[40]  James B. Orlin A Faster Strongly Polynomial Minimum Cost Flow Algorithm , 1993, Oper. Res..

[41]  C. Villani,et al.  Optimal Transportation and Applications , 2003 .

[42]  Julie Delon,et al.  Fast Transport Optimization for Monge Costs on the Circle , 2009, SIAM J. Appl. Math..

[43]  Michael H. F. Wilkinson,et al.  Shape representation and recognition through morphological curvature scale spaces , 2006, IEEE Transactions on Image Processing.

[44]  M. Cullen A Mathematical Theory of Large-scale Atmosphere/ocean Flow , 2006 .

[45]  François Pitié,et al.  Automated colour grading using colour distribution transfer , 2007, Comput. Vis. Image Underst..