Real-time interactive image segmentation using improved superpixels

Since it is very hard to use the conventional segmentation methods to extract an object with a variety of colors, interactive segmentation plays a more and more important role in image processing and is widely used in electronic products. However, performing interactive segmentation pixel by pixel always requires a lot of time. To improve the efficiency, in this paper, superpixels are applied instead of the original pixels for interactive segmentation. Moreover, since the accuracy may be reduced if the superpixel is applied, we also propose several ways to refine the superpixel. We use the variance to decide whether the superpixel should be refined. If so, the superpixel is re-segmented according to the color values of the adjacent superpixels. With the refinement, the edges of superpixels will highly match the boundaries of objects. Simulations show that using the refined superpixel instead of the original superpixel can achieve much better performance for interactive segmentation. Moreover, since the proposed algorithm is superpixel-based and the superpixels can be determined when users are drawing the lines, the computation time is much less than that of other interactive segmentation methods.

[1]  P.V.G.D. Prasad Reddy,et al.  A Comparative Analysis of Unsupervised K-Means, PSO and Self-Organizing PSO for Image Clustering , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[2]  M. Ganesan,et al.  A Novel Approach for the Analysis of Epileptic Spikes in EEG , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[3]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Rama Chellappa,et al.  Entropy rate superpixel segmentation , 2011, CVPR 2011.

[5]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Zheng Lin,et al.  Unseeded Region Growing for 3D Image Segmentation , 2000, VIP.

[7]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[8]  Harry Shum,et al.  Lazy snapping , 2004, ACM Trans. Graph..

[9]  Patrick Pérez,et al.  Interactive Image Segmentation Using an Adaptive GMMRF Model , 2004, ECCV.

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

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

[12]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[13]  Shih-Fu Chang,et al.  Segmentation using superpixels: A bipartite graph partitioning approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Maneesh Agrawala,et al.  Soft scissors: an interactive tool for realtime high quality matting , 2007, ACM Trans. Graph..

[16]  Camille Couprie,et al.  Power watersheds: A new image segmentation framework extending graph cuts, random walker and optimal spanning forest , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Guillermo Sapiro,et al.  A Geodesic Framework for Fast Interactive Image and Video Segmentation and Matting , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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