Improving User Control with Minimum Involvement in User-Guided Segmentation by Image Foresting Transform

The image foresting transform (IFT) can divide an image into object and background, each represented by one optimum-path forest rooted at internal and external markers selected by the user. We have considerably reduced the number of markers (user involvement) by separating object enhancement from its extraction. However, the user had no guidance about effective marker location during extraction, losing segmentation control. Now, we pre-segment the image automatically into a few regions. The regions inside the object are selected and merged from internal markers. Regions with object and background pixels are further divided by IFT. This provides more user control with minimum involvement, as validated on two public datasets.

[1]  Alexandre X. Falcão,et al.  The Ordered Queue and the Optimality of the Watershed Approaches , 2000, ISMM.

[2]  Vladimir Kolmogorov,et al.  Graph cut based image segmentation with connectivity priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Fernand Meyer,et al.  Levelings, Image Simplification Filters for Segmentation , 2004, Journal of Mathematical Imaging and Vision.

[4]  John I. Goutsias,et al.  Mathematical Morphology and its Applications to Image and Signal Processing , 2000, Computational Imaging and Vision.

[5]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[6]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004 .

[7]  Philippe Salembier,et al.  Antiextensive connected operators for image and sequence processing , 1998, IEEE Trans. Image Process..

[8]  Guillermo Sapiro,et al.  Interactive Image Segmentation via Adaptive Weighted Distances , 2007, IEEE Transactions on Image Processing.

[9]  João Paulo Papa,et al.  A Discrete Approach for Supervised Pattern Recognition , 2008, IWCIA.

[10]  Edward R. Dougherty,et al.  Mathematical Morphology in Image Processing , 1992 .

[11]  Jayaram K. Udupa,et al.  An ultra-fast user-steered image segmentation paradigm: live wire on the fly , 2000, IEEE Transactions on Medical Imaging.

[12]  Javier A. Montoya-Zegarra,et al.  Fast interactive segmentation of natural images using the image foresting transform , 2009, 2009 16th International Conference on Digital Signal Processing.

[13]  Vladimir Kolmogorov,et al.  "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..