The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.09 - Sept. (2012 vol.34)
pp: 1731-1743
Yi Yang , Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
S. Hallman , Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
D. Ramanan , Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
C. C. Fowlkes , Dept. of Comput. Sci., Univ. of California at Irvine, Irvine, CA, USA
ABSTRACT
We formulate a layered model for object detection and image segmentation. We describe a generative probabilistic model that composites the output of a bank of object detectors in order to define shape masks and explain the appearance, depth ordering, and labels of all pixels in an image. Notably, our system estimates both class labels and object instance labels. Building on previous benchmark criteria for object detection and image segmentation, we define a novel score that evaluates both class and instance segmentation. We evaluate our system on the PASCAL 2009 and 2010 segmentation challenge data sets and show good test results with state-of-the-art performance in several categories, including segmenting humans.
INDEX TERMS
probability, image segmentation, object detection, human segmentation, layered object models, image segmentation, object detection, generative probabilistic model, object detectors, shape masks, appearance, depth ordering, image pixel labelling, class label estimation, object instance label estimation, class segmentation, instance segmentation, PASCAL 2009 segmentation challenge data sets, PASCAL 2010 segmentation challenge data sets, Shape, Image segmentation, Image color analysis, Detectors, Object detection, Mathematical model, Computational modeling, segmentation benchmark., Image segmentation, multiclass object detection, layered model, 2.1D model
CITATION
Yi Yang, S. Hallman, D. Ramanan, C. C. Fowlkes, "Layered Object Models for Image Segmentation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 9, pp. 1731-1743, Sept. 2012, doi:10.1109/TPAMI.2011.208
REFERENCES
[1] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman, "The PASCAL Visual Object Classes Challenge 2010 (VOC '10) Results," 2010.
[2] P.A. Viola and M.J. Jones, "Robust Real-Time Face Detection," Int'l J. Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
[3] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 886-893, 2005.
[4] P.F. Felzenszwalb, R.B. Girshick, D. McAllester, and D. Ramanan, "Object Detection with Discriminatively Trained Part Based Models," IEEE Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, Sept. 2010.
[5] X. He, R. Zemel, and M. Carreira-Perpinan, "Multiscale Conditional Random Fields for Image Labeling," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, 2004.
[6] A. Torralba, K. Murphy, and W. Freeman, "Contextual Models for Object Detection Using Boosted Random Fields," Proc. Advances in Neural Information Processing Systems, 2004.
[7] S. Kumar and M. Hebert, "A Hierarchical Field Framework for Unified Context-Based Classification," Proc. 10th IEEE Int'l Conf. Computer Vision, vol. 2, 2005.
[8] J. Shotton, J. Winn, C. Rother, and A. Criminisi, "Textonboost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation," Proc. European Conf. Computer Vision, vol. 3951, p. 1, 2006.
[9] Z. Tu, "Auto-Context and Its Application to High-Level Vision Tasks," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[10] M. Kumar, P. Torr, and A. Zisserman, "Obj Cut," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, 2005.
[11] D. Ramanan, "Using Segmentation to Verify Object Hypotheses," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[12] S. Yu, R. Gross, and J. Shi, "Concurrent Object Recognition and Segmentation by Graph Partitioning," Proc. Neural Information Processing Systems, pp. 1407-1414, 2003.
[13] B. Leibe, A. Leonardis, and B. Schiele, "Combined Object Categorization and Segmentation with an Implicit Shape Model," Proc. European Conf. Computer Vision Workshop Statistical Learning in Computer Vision, pp. 17-32, 2004.
[14] A. Levin and Y. Weiss, "Learning to Combine Bottom-Up and Top-Down Segmentation," Int'l J. Computer Vision, vol. 81, no. 1, pp. 105-118, 2009.
[15] Z. Tu, X. Chen, A. Yuille, and S. Zhu, "Image Parsing: Unifying Segmentation, Detection, and Recognition," Int'l J. Computer Vision, vol. 63, no. 2, pp. 113-140, 2005.
[16] X. Ren, C. Fowlkes, and J. Malik, "Cue Integration for Figure/Ground Labeling" Proc. Neural Information Processing Systems, 2005.
[17] L. Ladicky, P. Sturgess, K. Alahari, C. Russell, and P.H.S. Torr, "What, Where and How Many? Combining Object Detectors and Crfs," Proc. 11th European Conf. Computer Vision, pp. 424-437, 2010.
[18] T. Brox, L. Bourdev, S. Maji, and J. Malik, "Object Segmentation by Alignment of Poselet Activations to Image Contours," Proc. IEEE Conf. Computer Vision and Pattern Recognition, http://www.eecs. berkeley.edu/lbourdevposelets , 2011.
[19] L. Bourdev, S. Maji, T. Brox, and J. Malik, "Detecting People Using Mutually Consistent Poselet Activations," Proc. European Conf. Computer Vision, http://www.eecs.berkeley.edu/lbour devposelets , 2010.
[20] J. Wang and E. Adelson, "Representing Moving Images with Layers," IEEE Trans. Image Processing, vol. 3, no. 5, pp. 625-638, Sept. 1994.
[21] N. Jojic and B. Frey, "Learning Flexible Sprites in Video Layers," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, 2001.
[22] M. Kumar, P. Torr, and A. Zisserman, "Learning Layered Pictorial Structures from Video," Proc. Indian Conf. Computer Visual, Graphics and Image, pp. 158-163, 2004.
[23] M. Nitzberg, D. Mumford, and T. Shiota, Filtering, Segmentation and Depth. Springer, 1993.
[24] R.-X. Gao, T.-F. Wu, S.-C. Zhu, and N. Sang, "Bayesian Inference for Layer Representation with Mixed Markov Random Field," Proc. Sixth Int'l Conf. Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 213-224, 2007.
[25] I. Liechter and M. Lindenbaum, "Boundary Ownership by Lifting to 2.1D," Proc. 12th IEEE Int'l Conf. Computer Vision, 2009.
[26] J. Winn and J. Shotton, "The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
[27] X. Ren, C. Fowlkes, and J. Malik, "Figure/Ground Assignment in Natural Images," Proc. Ninth European Conf. Computer Vision, 2006.
[28] D. Hoiem, C. Rother, and J. Winn, "3D Layout CRF for Multi-View Object Class Recognition and Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[29] M. Maire, "Simultaneous Segmentation and Figure/Ground Organization Using Angular Embedding" Proc. 11th European Conf. Computer Vision, 2010.
[30] D. Hoiem, A. Stein, A. Efros, and M. Hebert, "Recovering Occlusion Boundaries from a Single Image," Proc. 11th IEEE Int'l Conf. Computer Vision, 2007.
[31] A. Saxena, M. Sun, and A. Ng, "Make3D: Learning 3D Scene Structure from a Single Still Image," IEEE Trans. Pattern and Machine Intelligence, vol. 31, no. 5, pp. 824-840, May 2009.
[32] Y. Yang, S. Hallman, D. Ramanan, and C. Fowlkes, "Layered Object Detection for Multi-Class Segmentation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[33] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, "From Contours to Regions: An Empirical Evaluation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[34] S. Li, "Markov Random Field Models in Computer Vision," Proc. Third European Conf. Computer Vision, pp. 361-370, 1994.
[35] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes Challenge 2009 (VOC '09) Results," 2009.
[36] P.F. Felzenszwalb, R.B. Girshick, and D. McAllester, "Discriminatively Trained Deformable Part Models, Release 4," http://people.cs.uchicago.edu/pfflatent-release4 /, 2012.
[37] P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, "Contour Detection and Hierarchical Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, May 2011.
[38] F. Li, J. Carreira, and C. Sminchisescu, "Object Recognition as Ranking Holistic Figure-Ground Hypotheses," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010.
23 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool