Fast Object Detection Using Steiner Tree

• Autorzy: Xiaoyin, D.
• Języki publikacji: EN

Abstrakty

EN

We propose an approach to speed-up object detection, with an emphasis on settings where multiple object classes are detected. Our method uses a segmentation algorithm to select a small number of image regions on which to run a classifier. Compared to the classical sliding window approach, a significantly smaller number of rectangles is examined, which yields significantly faster object detection. Further, in the multiple object class setting, we show that the computational cost of segmentations can be amortized across objects classes, resulting in an additional speedup. At the heart of our approach is reduction to a directed Steiner tree optimization problem, which we solve approximately in order to select the segmentation algorithm parameters. The solution gives a small set of segmentation strategies that can be shared across object classes. Compared to the sliding window approach, our method results in two orders of magnitude fewer regions considered, and significant (10-15x) computational time speedups on challenging object detection datasets (LabelMe and StreetScenes) while maintaining comparable detection accuracy.

Słowa kluczowe

EN

object; detection; recognition; Steiner tree

• Czasopismo: Machine Graphics and Vision
• Rocznik: 2010
• Tom: Vol. 19, No. 2
• Strony: 127-142
• Opis fizyczny: Bibliogr. 36 poz., wykr.

Twórcy

autor

Xiaoyin, D.

• Daunmu Xiaoyin Technique and Quality Department, Nanjing Electrical Equipment Ltd.,

Bibliografia

• [1] M. R. Garey. and D. S. Johnson. Computers and Intractability: A guide to the theory of NP-completeness. W. H. Freeman Co.: New York, NY. USA, 1990.
• [2] R. Hwang, D. Richards, and P. Winter. The Steiner Tree problem. Annals of Discrete Mathematics. 53, 1992.
• [3] R. Caruana. Multitask learning: A knowledge-based source of inductive bias. Proc. International Conference on Machine Learning, pp. 41-48, 1993.
• [4] H. A. Rowley, S. Baluja, and T. Kanade. Human face detection in visual scenes. Proc. Neural Information Processing Systems, 1996.
• [5] A. Zelikovsky. A series of approximation algorithms for the acyclic directed Steiner tree problem. Algorithmica. 18, 1997.
• [6] L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254-1259, 1998.
• [7] M. Charikar, C. Chekuri, T. Cheung, Z. Dai, A. Goel, and S. Guha. Approximation algorithms for directed Steiner problems. Journal of Algorithms, 33(1):73-91, 1999.
• [8] E. Sharon, A. Brandt, and R. Basri. Fast multiscale image segmentation. Proc. International Conference on Computer Vision, pp. 70-77, 1999.
• [9] T. Kadir and M. Brady. Saliency, scale and image description. International Journal of Computer Vision. 45(2):83-105, 2001.
• [10] L. Zosin and S. Khuller. On directed Steiner trees. Proc. Symposium on Discrete Algorithms, pp. 59-63, 2002.
• [11] D. E. Drake and S. Hougardy. On approximation algorithms for the terminal Steiner tree problem, Information Processing Letters, 89(1), 2004.
• [12] P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 2004.
• [13] P. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2), 2004.
• [14] J. Wu, J. M. Rehg, and M. D. Mullin. Learning a rare event detection cascade by direct feature selection. Proc. Neural Information Processing Systems, pp. 1523-1530, 2004.
• [15] N. Dalai and B. Triggs. Histograms of oriented gradients for human detection. Proc. Computer Vision and Pattern Recognition, 2005.
• [16] D. Walther, U. Rutishauser, C. Koch, and P. Perona. Selective visual attention enables learning and recognition of multiple objects in cluttered scenes, Computer Vision and Image Understanding, 2005.
• [17] X. C. Z. Tu, A. L. Yuille, and S. C. Zhu. Image parsing: Unifying segmentation, detection, and recognition. International Journal of Computer Vision, 63(2)., 2005
• [18] S. Bileschi. StreetScenes: Towards scene understanding in still images. PhD thesis. Massachusetts Institute of Technology, 2006.
• [19] O. Chum and A. Zisserman. An exemplar model for learning object classes. Proc. Computer Vision and Pattern Recognition, 19-21, 2007.
• [20] V. Melkonian. New primal-dual algorithms for Steiner Tree problems. Computers arid Operations Research. 34(7), 2007.
• [21] A. Torralba, K. P. Murphy, and W. T. Freeman. Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(5):854-869, 2007.
• [22] B. Wu, and R. Nevatia. Simultaneous object detection and segmentation by boosting local shape feature based classifier. Proc. Computer Vision and Pattern Recognition, pp. 1-8, 2007.
• [23] P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale. Deformable part model. Proc. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
• [24] G. Heitz, S. Gould, A. Saxena, and D. Roller. Cascaded classification models: Combining models for holistic scene understanding, Proc. Neural Information Processing Systems, pp. 1-8, 2008.
• [25] C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by Efficient Subwindow Search, Proc. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
• [26] D. Larlus and F. Jurie. Combining appearance models and markov random fields for category level object segmentation. Proc. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
• [27] B. Leibe, A. Leonardis, and B. Schiele. Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision, vol. 77. no. 3. pp. 259-289, 2008.
• [28] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freemane. LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision. 77:157-173, 2008.
• [29] B. Fulkcrson, A. Vedaldi, and S. Soatto. Class segmentation and object localization with superpixel neighborhoods, Proc. International Conference on Computer Vision, pp. 1-8, 2009.
• [30] J. Gall, and V. Lempitsky. Class-specific Hough forests for object detection. Proc. Computer Vision and Pattern Recognition, pp. 1022-1029, 2009.
• [31] S. Gould, T. Gao, and D. Roller. Region-based segmentation and object detection. Proc. Neural, 2009
• [32] C. Gu, J. J. Lini, P. Arbelaez, and J. Malik. Recognition using regions. Proc. Computer Vision and Pattern Recognition, pp. 1080-1037, 2009
• [33] H. Harzallah, F. Jurie, and C. Schmid. Combining efficient object localization and image classification. Proc. Computer Vision and Pattern Recognition, pp. 237-244., 2009
• [34] L. J. Li, R. Socher, and L. Fei-Fei. Towards total scene understanding: Classification, annotation and segmentation in an automatic framework, Proc. Computer Vision and Pattern Recognition, pp. 2036-2043, 2009.
• [35] S. Maji and J. Malik. Object detection using a max-margin hough transform. Proc. Computer Vision and Pattern Recognition, pp. 1038-1045, 2009.
• [36] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge Results, http://pascallin.ecs.soton.ac.uk/challenges/VOC, 2007-2009.