Acceleration Strategies for Gaussian Mean-Shift Image Segmentation

Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude.

[1]  Andrew W. Moore,et al.  Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees , 1998, NIPS.

[2]  Miguel Á. Carreira-Perpiñán,et al.  On the Number of Modes of a Gaussian Mixture , 2003, Scale-Space.

[3]  Larry S. Davis,et al.  Mean-shift analysis using quasiNewton methods , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[4]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Daniel DeMenthon,et al.  SPATIO-TEMPORAL SEGMENTATION OF VIDEO BY HIERARCHICAL MEAN SHIFT ANALYSIS , 2002 .

[6]  Miguel Á. Carreira-Perpiñán,et al.  Gaussian Mean-Shift Is an EM Algorithm , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[8]  Larry S. Davis,et al.  Efficient Kernel Density Estimation Using the Fast Gauss Transform with Applications to Color Modeling and Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Ilan Shimshoni,et al.  Mean shift based clustering in high dimensions: a texture classification example , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[11]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[12]  Miguel Á. Carreira-Perpiñán,et al.  Mode-Finding for Mixtures of Gaussian Distributions , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Leslie Greengard,et al.  The Fast Gauss Transform , 1991, SIAM J. Sci. Comput..

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

[15]  Larry S. Davis,et al.  Improved fast gauss transform and efficient kernel density estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.