Appearance-based keypoint clustering

We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that belong to separate objects or background regions. These clusters are then used to constrain the matching of keypoints over pairs of images, resulting in greatly improved matching under difficult conditions. We present a thorough evaluation of each component of the algorithm, and show its usefulness on difficult matching problems.

[1]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Brendan J. Frey,et al.  Epitomic analysis of appearance and shape , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[5]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[6]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[7]  Gérard G. Medioni,et al.  Inferring global pereeptual contours from local features , 1996, International Journal of Computer Vision.

[8]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[9]  Ronen Basri,et al.  Texture segmentation by multiscale aggregation of filter responses and shape elements , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Vincent Lepetit,et al.  Feature Harvesting for Tracking-by-Detection , 2006, ECCV.

[11]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Jianbo Shi,et al.  Learning Segmentation by Random Walks , 2000, NIPS.

[13]  Allan D. Jepson,et al.  Quantitative evaluation of a novel image segmentation algorithm , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  F. Estrada Advances in computational image segmentation and perceptual grouping , 2005 .

[15]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2005, International Journal of Computer Vision.

[16]  Carsten Rother,et al.  Clustering appearance and shape by learning jigsaws , 2006, NIPS.

[17]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Lance R. Williams,et al.  Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience , 1997, Neural Computation.

[19]  Jitendra Malik,et al.  Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[21]  Michael Werman,et al.  Self-Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database Organization , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Lin Yang,et al.  Multiple Class Segmentation Using A Unified Framework over Mean-Shift Patches , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Allan D. Jepson,et al.  Spectral Embedding and Min Cut for Image Segmentation , 2004, BMVC.

[24]  Tomás Pajdla,et al.  Multi-label image segmentation via max-sum solver , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Joachim M. Buhmann,et al.  Empirical Evaluation of Dissimilarity Measures for Color and Texture , 2001, Comput. Vis. Image Underst..