This Article 
 Bibliographic References 
 Add to: 
A Similarity Metric for Edge Images
October 2003 (vol. 25 no. 10)
pp. 1265-1273

Abstract—The performance of several discrepancy measures for the comparison of edge images is analyzed and a novel similarity metric aimed at overcoming their problems is proposed. The algorithm finds an optimal matching of the pixels between the images and estimates the error produced by this matching. The resulting Pixel Correspondence Metric (PCM) can take into account edge strength as well as the displacement of edge pixel positions in the estimation of similarity. A series of experimental tests shows the new metric to be a robust and effective tool in the comparison of edge images when a small localization error of the detected edges is allowed.

[1] Y.J. Zhang, A Survey on Evaluation Methods for Image Segmentation Pattern Recognition, vol. 29, no. 8, pp. 1335-1346, 1996.
[2] L. Kitchen and A. Rosenfeld, Edge Evaluation Using Local Edge Coherence IEEE Trans. Systems, Man, and Cybernetics, vol. 11, no. 9, pp. 597-605, Sept. 1981.
[3] A.J. Baddeley, Errors in Binary Images and an$L^p$Version of the Hausdorff Metric Nieuw Archief voor Wiskunde, vol. 10, pp. 157-183, 1992.
[4] Q. Zhu, Improving Edge Detection by an Objective Edge Evaluation Proc. 1992 ACM/SIGAPP Symp. Applied Computing, pp. 459-468, 1992.
[5] M.D. Heath, S. Sarkar, T. Sanocki, and K.W. Bowyer, A Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 12, pp. 1338-1359, Dec. 1997.
[6] T.B. Nguyen and D. Ziou, Contextual and Non-Contextual Performance Evaluation of Edge Detectors Pattern Recognition Letters, vol. 21, no. 8, pp. 805-816, 2000.
[7] G. Liu and R.M. Haralick, Optimal Matching Problem in Detection and Recognition Performance Evaluation Pattern Recognition, vol. 35, pp. 2125-2139, 2002.
[8] W. K. Pratt, Digital Image Processing. New York: Wiley-Interscience 1978
[9] K.W. Bowyer, C. Kranenburg, and S. Dougherty, Edge Detector Evaluation Using Empirical ROC Curves Computer Vision and Image Understanding, vol. 84, pp. 77-103, 2001.
[10] H.W. Kuhn, The Hungarian Method for the Assignment Problem Naval Research Logistics Quarterly, vol. 3, pp. 253-258, Oct. 1956.
[11] H.A.B. Saip and C.L. Lucchesi, Matching Algorithms for Bipartite Graphs Technical Report, IMECC, Universidade Estadual de Campinas, Brazil, 1993.
[12] H.N. Gabow and R.E. Tarjan, Faster Scaling Algorithms for Network Problems SIAM J. Computing, vol. 18, no. 5, pp. 1013-1036, Oct. 1989.
[13] Test Images for Image Processing Purposes Hungarian Association for Image Analysis and Pattern Recognition (KEPAF), Available at: ~kepaf/TestImages. 2003.
[14] J. Canny, A Computational Approach to Edge Detection IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
[15] T.M. Sheen, Tools for Portable Parallel Image Processing PhD dissertation, Univ. of Aberdeen, 1999.

Index Terms:
Edge detection, image similarity, performance evaluation, pixel correspondence metric, weighted matching in bipartite graphs, matching problem, assignment problem.
Miguel Segui Prieto, Alastair R. Allen, "A Similarity Metric for Edge Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1265-1273, Oct. 2003, doi:10.1109/TPAMI.2003.1233900
Usage of this product signifies your acceptance of the Terms of Use.