The Community for Technology Leaders
RSS Icon
Issue No.02 - February (2011 vol.33)
pp: 256-265
Igor Kleiner , University of Haifa, Haifa
Daniel Keren , University of Haifa, Haifa
Ilan Newman , University of Haifa, Haifa
Oren Ben-Zwi , University of Haifa, Haifa
Property testing is a rapidly growing field of research. Typically, a property testing algorithm proceeds by quickly determining whether an input can satisfy some condition, under the assumption that most inputs do not satisfy it. If the input is "far” from satisfying the condition, the algorithm is guaranteed to reject it with high probability. Applying this paradigm to image detection is desirable since images are large objects and a lot of time can be saved by quickly rejecting images which are "far” from satisfying a certain condition the user is interested in. Further, typically most inputs are, indeed, "far” from the sought images. We demonstrate this by analyzing the problem of deciding whether a binary image can be partitioned according to a template represented by a rectangular grid, and introduce a quick "rejector,” which tests an image extracted from the input image, but whose size, as well as the time required to construct it, are constants which are independent of the input image size. With high probability, the rejector dismisses the inputs which are "far” from the template.
Property testing, image partitioning.
Igor Kleiner, Daniel Keren, Ilan Newman, Oren Ben-Zwi, "Applying Property Testing to an Image Partitioning Problem", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 2, pp. 256-265, February 2011, doi:10.1109/TPAMI.2010.165
[1] N. Alon and J. Spencer, The Probabilistic Method. John Wiley and Sons, 2000.
[2] S. Baker and S.K. Nayar, "Pattern Rejection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 544-549, 1996.
[3] H. Chernoff, "A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the Sum of Observations," Annals of Math. Statistics, vol. 23, pp. 493-507, 1952.
[4] M. Elad, Y. Hel Or, and R. Keshet, "Rejection Based Classifier for Face Detection," Pattern Recognition Letters, vol. 23, no. 12, pp. 1459-1471, Oct. 2002.
[5] E. Fischer, "The Art of Uninformed Decisions: A Primer to Property Testing," Bull. European Assoc. for Theoretical Computer Science, Computational Complexity Column, vol. 75, pp. 97-126, 2001.
[6] O. Goldreich, S. Goldwasser, and D. Ron, "Property Testing and Its Connection to Learning and Approximation," J. ACM, vol. 45, pp. 653-750, 1998.
[7] T. Hagerup and C. Rueb, "A Guided Tour of Chernoff Bounds," Information Processing Letters, vol. 33, no. 6, pp. 305-308, 1990.
[8] Y. Hel-Or and H. Hel-Or, "Real-Time Pattern Matching Using Projection Kernels," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 9, pp. 1430-1445, Sept. 2005.
[9] D. Keren, M. Osadchy, and C. Gotsman, "Antifaces: A Novel, Fast Method for Image Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp. 747-761, July 2001.
[10] A.B. Lee, K.S. Pedersen, and D. Mumford, "The Nonlinear Statistics of High-Contrast Patches in Natural Images," Int'l J. Computer Vision, vol. 54, nos. 1-3, pp. 83-103, Aug. 2003.
[11] M. Lindenbaum, "An Integrated Model for Evaluating the Amount of Data Required for Reliable Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1251-1264, Nov. 1997.
[12] S. Raskhodnikova, "Approximate Testing of Visual Properties," Proc. Sixth Int'l Workshop Approximation Algorithms for Combinatorial Optimization Problems, pp. 370-381, 2003.
[13] M. Ratsch, G. Teschke, S. Romdhani, and T. Vetter, "Wavelet Frame Accelerated Reduced Support Vector Machines," IEEE Trans. Image Processing, vol. 17, no. 12, pp. 2456-2464, Dec. 2008.
[14] S. Romdhani, P. Torr, B. Schoelkopf, and A. Blake, "Efficient Face Detection by a Cascaded Support-Vector," Proc. Royal Soc. London, vol. 460, no. 2501, pp. 3283-3297, 2004.
[15] H. Sahbi and D. Geman, "A Hierarchy of Support Vector Machines for Pattern Detection," J. Machine Learning Research, vol. 7, pp. 2087-2123, 2006.
[16] J. Sun, J.M. Rehg, and A.F. Bobick, "Automatic Cascade Training with Perturbation Bias," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 276-283, 2004.
[17] P. Viola and M.J. Jones, "Robust Real-Time Face Detection," Int'l J. Computer Vision, vol. 57, no. 2, pp. 137-154, May 2004.
[18] J. Vogel and B. Schiele, "Semantic Modeling of Natural Scenes for Content-Based Image Retrieval," Int'l J. Computer Vision, vol. 72, no. 2, pp. 133-157, Apr. 2007.
[19] Y. Weiss and W.T. Freeman, "What Makes a Good Model of Natural Images?" Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
46 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool