nips nips2006 nips2006-160 nips2006-160-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Graham Mcneill, Sethu Vijayakumar
Abstract: Correspondence algorithms typically struggle with shapes that display part-based variation. We present a probabilistic approach that matches shapes using independent part transformations, where the parts themselves are learnt during matching. Ideas from semi-supervised learning are used to bias the algorithm towards finding ‘perceptually valid’ part structures. Shapes are represented by unlabeled point sets of arbitrary size and a background component is used to handle occlusion, local dissimilarity and clutter. Thus, unlike many shape matching techniques, our approach can be applied to shapes extracted from real images. Model parameters are estimated using an EM algorithm that alternates between finding a soft correspondence and computing the optimal part transformations using Procrustes analysis.
[1] S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. PAMI, 24:509–522, 2002.
[2] H. Chui and A. Rangarajan. A new point matching algorithm for non-rigid registration. Comp. Vis. and Image Understanding, 89:114–141, 2003.
[3] Z. Tu and A.L. Yuille. Shape matching and recognition using generative models and informative features. In ECCV, 2004.
[4] G. McNeill and S. Vijayakumar. A probabilistic approach to robust shape matching. In ICIP, 2006.
[5] Noam Shental, Aharon Bar-Hillel, Tomer Hertz, and Daphna Weinshall. Computing Gaussian mixture models with EM using equivalence constraints. In NIPS. 2004.
[6] Kaleem Siddiqi and Benjamin B. Kimia. Parts of visual form: Computational aspects. PAMI, 17(3):239– 251, 1995.
[7] M. Titsias. Unsupervised Learning of Multiple Objects in Images. PhD thesis, Univ. of Edinburgh, 2005.
[8] B. Luo and E.R. Hancock. A unified framework for alignment and correspondence. Computer Vision and Image Understanding, 92(26-55), 2003.
[9] H. Ling and D.W. Jacobs. Using the inner-distance for classification of ariculated shapes. In CVPR, 2005.