The Community for Technology Leaders
RSS Icon
Issue No.03 - March (2008 vol.30)
pp: 451-462
In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical Euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.
Image classification, Information retrieval, Pattern recognition, Artificial intelligence, Algorithms
Jie Yu, Jaume Amores, Nicu Sebe, Petia Radeva, Qi Tian, "Distance Learning for Similarity Estimation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 3, pp. 451-462, March 2008, doi:10.1109/TPAMI.2007.70714
[1] M. Wallach, “On Psychological Similarity,” Psychological Rev., vol. 65, no. 2, pp. 103-116, 1958.
[2] A. Tversky and D.H. Krantz, “The Dimensional Representation and the Metric Structure of Similarity Data,” J. Math. Psychology, vol. 7, pp. 572-597, 1977.
[3] A. Tversky, “Features of Similarity,” Psychological Rev., vol. 84, no. 4, pp. 327-352, 1977.
[4] N.S. Chang and K.S. Fu, “Query by Pictorial Example,” IEEE Trans. Software Eng., vol. 6, no. 6, pp. 519-524, June 1980.
[5] P. Aigrain, “Organizing Image Banks for Visual Access: Model and Techniques,” Proc. Int'l Meeting for Optical Publishing and Storage, pp. 257-270, 1987.
[6] K. Kato, “Database Architecture for Content-Based Image Retrieval,” Proc. SPIE Conf. Image Storage and Retrieval Systems, vol. 1662, pp. 112-123, 1992.
[7] M. Flicker, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by Image and Video Content: The QBIC System,” Computer, vol. 28, no. 9, pp. 23-32, Sept. 1995.
[8] V.N. Gudivada and V. Raghavan, “Design and Evaluation of Algorithms for Image Retrieval by Spatial Similarity,” ACM Trans. Information Systems, vol. 13, no. 2, pp. 115-144, 1995.
[9] M. Zakai, “General Distance Criteria,” IEEE Trans. Information Theory, pp. 94-95, Jan. 1964.
[10] N. Sebe, M.S. Lew, and D.P. Huijsmans, “Toward Improved Ranking Metrics,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1132-1143, Oct. 2000.
[11] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 22, no. 12, pp.1349-1380, Dec. 2000.
[12] M. Swain and D. Ballard, “Color Indexing,” Int'l J. Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[13] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Texture Features for Image Classification,” IEEE Trans. Systems, Man, and Cybernetics, vol. 3, no. 6, pp. 610-621, 1973.
[14] J.R. Smith and S.F. Chang, “Transform Features for Texture Classification and Discrimination in Large Image Database,” Proc. IEEE Int'l Conf. Image Processing, 1994.
[15] B.M. Mehtre, M. Kankanhalli, and W.F. Lee, “Shape Measures for Content Based Image Retrieval: A Comparison,” Information Processing Management, vol. 33, no. 3, pp. 319-337, 1997.
[16] Q. Tian, Q. Xue, J. Yu, N. Sebe, and T.S. Huang, “Toward an Improved Error Metric,” Proc. IEEE Int'l Conf. Image Processing, Oct. 2004.
[17] J. Amores, N. Sebe, and P. Radeva, “Boosting the Distance Estimation: Application to the K-Nearest Neighbor Classifier,” Pattern Recognition Letters, vol. 27, no. 3, pp. 201-209, Feb. 2006.
[18] J. Yu, J. Amores, N. Sebe, and Q. Tian, “Toward Robust Distance Metric Analysis for Similarity Estimation,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, June 2006.
[19] R. Haralick and L. Shapiro, Computer and Robot Vision II. Addison-Wesley, 1993.
[20] I.T. Jolliffe, Principal Component Analysis, second ed. Springer, 2002.
[21] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. John Wiley & Sons, 2001.
[22] R.E. Schapire and Y. Singer, “Improved Boosting Using Confidence-Rated Predictions,” Machine Learning, vol. 37, no. 3, pp.297-336, 1999.
[23] D.W. Jacobs, D. Weinshall, and Y. Gdalyahu, “Classification with Nonmetric Distances: Image Retrieval and Class Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 583-600, June 2000.
[24] P.J. Phillips, “Support Vector Machines Applied to Face Recognition,” Proc. Advances in Neural Information Processing Systems, vol. 11, 1998.
[25] P. Viola and M.J. Jones, “Robust-Real Time Face Detection,” Int'l J.Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
[26] B. Moghaddam, T. Jebara, and A. Pentland, “Bayesian Face Recognition,” Pattern Recognition, 2000.
[27] T.M. Cover and P.E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. Information Theory, vol. 13, pp. 21-27, Jan. 1968.
[28] C. Domeniconi, J. Peng, and D. Gunopulos, “Locally Adaptive Metric Nearest Neighbor Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1281-1285, Sept. 2002.
[29] J. Peng, D. Heisterkamp, and H.K. Dai, “LDA/SVM Driven Nearest Neighbor Classification,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 940-942, 2001.
[30] E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell, “Distance Metric Learning, with Application to Clustering with Side-Information,” Proc. Neural Information Processing Systems, pp. 505-512, 2003.
[31] A. Bar-Hillel, T. Hertz, N. Shental, and D. Weinshall, “Learning Distance Functions Using Equivalence Relations,” Proc. Int'l Conf. Machine Learning, pp. 11-18, 2003.
[32] V. Athitsos, J. Alon, S. Sclaroff, and G. Kollios, “BoostMap: A Method for Efficient Approximate Similarity Rankings,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[33] T. Hertz, A. Bar-Hillel, and D. Weinshall, “Learning Distance Functions for Image Retrieval,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 570-577, 2004.
[34] P.J. Huber, Robust Statistics. John Wiley & Sons, 1981.
[35] M.S. Lew, T.S. Huang, and K. Wong, “Learning and Feature Selection in Stereo Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 9, pp. 869-882, Sept. 1994.
[36] L. Tang, Y. Kong, L.S. Chen, C.R. Lansing, and T.S. Huang, “Performance Evaluation of a Facial Feature Tracking Algorithm,” Proc. NSF/ARPA Workshop: Performance versus Methodology in Computer Vision, 1994.
[37] K. Fukunaga and J. Mantock, “Nonparametric Discriminant Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 671-678, Feb. 1997.
[38] Y. LeCun and C. Cortes, “MNIST Database,” http://yann.lecun. com/exdbmnist/, 1998.
[39] A. Martinez and R. Benavente, The AR Face Database, technical report, vol. 24, Computer Vision Center, 1998.
[40] J. Matas, M. Hamouz, K. Jonsson, J. Kittler, C. Kotropoulos, A. Tefas, I. Pitas, T. Tan, H. Yan, F. Smeraldi, J. Bigun, N. Capdevielle, W. Gerstner, S. Ben-Yacoub, Y. Abdeljaoued, and E. Mayoraz, “Comparison of Face Verification Results on the Xm2vts Database,” Proc. Int'l Conf. Pattern Recognition, pp. 858-863, 1999.
[41] J. Yu and Q. Tian, “Constructing Discriminant and Descriptive Features for Face Classification,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, May 2006.
[42] C. Merz and P. Murphy, “UCI Repository of Machine Learning Databases,” html , 1998.
[43] J.R. Quinlan, “Bagging, Boosting, and C4.5,” Proc. Nat'l Conf. Artificial Intelligence, pp. 725-730, 1996.
[44] D.G. Stork and E. Yom-Tov, “Computer Manual in MATLAB to Accompany,” Pattern Classification, John Wiley & Sons, 2004.
[45] T. Hertz, A. Hillel, and D. Weinshall, “Learning a Kernel Function for Classification with Small Training Samples,” Proc. ACM Int'l Conf. Machine Learning, 2006.
[46] Y. Rubner, C. Tomasi, and L.J. Guibas, “The Earth Mover's Distance as a Metric for Image Retrieval,” Int'l J. Computer Vision, 2000.
[47] J. Lafferty, V. Pietra, and S. Pietra, “Statistical Learning Algorithms Based on Bregman Distances,” Proc. Canadian Workshop Information Theory, 1997.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool