Superpixel segmentation based on multiple seed growth

The purpose of the superpixel algorithm is to find an over-segmented set of images. In this paper, we transform the superpixel segmentation problem into the problem of minimizing the cost function on the graph. We propose a new minimum spanning tree cost function based on graph theory. In order to achieve this cost function, we propose a greedy algorithm based on multiple seed growth. Compared to other superpixel segmentation algorithms, our proposed algorithm not only ensures superpixel segmentation quality but also has linear execution time and is easy to implement. Experiments on the BSD benchmark dataset show that superpixel segmentation algorithm in this paper is superior to the current most of the superpixel segmentation algorithm in Boundary recall and Under-segmentation error, and the algorithm is more efficient than the current most of the superpixel segmentation algorithm.

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

[2]  Pushmeet Kohli,et al.  Exact inference in multi-label CRFs with higher order cliques , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[4]  Luc Van Gool,et al.  SEEDS: Superpixels Extracted via Energy-Driven Sampling , 2012, ECCV.

[5]  Peer Neubert,et al.  Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms , 2014, 2014 22nd International Conference on Pattern Recognition.

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

[7]  Xuelong Li,et al.  Lazy Random Walks for Superpixel Segmentation , 2014, IEEE Transactions on Image Processing.

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

[9]  Bohyung Han,et al.  Generalized Background Subtraction Using Superpixels with Label Integrated Motion Estimation , 2014, ECCV.

[10]  Stewart Burn,et al.  Superpixels via pseudo-Boolean optimization , 2011, 2011 International Conference on Computer Vision.

[11]  Bastian Leibe,et al.  Superpixels: An evaluation of the state-of-the-art , 2016, Comput. Vis. Image Underst..

[12]  Pushmeet Kohli,et al.  Robust Higher Order Potentials for Enforcing Label Consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Stefano Soatto,et al.  Quick Shift and Kernel Methods for Mode Seeking , 2008, ECCV.

[14]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

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

[17]  Peer Neubert,et al.  Superpixels and their Application for Visual Place Recognition in Changing Environments , 2015 .

[18]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, CVPR 2004.

[19]  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.

[20]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  Iasonas Kokkinos,et al.  Segmentation-Aware Deformable Part Models , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  政子 鶴岡,et al.  1998 IEEE International Conference on SMCに参加して , 1998 .

[25]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.