Peekaboo-Where are the Objects? Structure Adjusting Superpixels

This paper addresses the search for a fast and meaningful image segmentation in the context of k-means clustering. The proposed method builds on a widely-used local version of Lloyd's algorithm, called Simple Linear Iterative Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically adjust the local search, adopting superpixel resolution dynamically to structure existent in the image, and thus provides for more meaningful superpixels in the same linear runtime as standard SLIC. The proposed method is evaluated against state-of-the-art techniques and improved boundary adherence and undersegmentation error are observed, whilst still remaining among the fastest algorithms which are tested.

[1]  Guosheng Lin,et al.  Deep convolutional neural fields for depth estimation from a single image , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Huchuan Lu,et al.  Superpixel tracking , 2011, 2011 International Conference on Computer Vision.

[3]  Andreas Geiger,et al.  Object scene flow for autonomous vehicles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Stephen Gould,et al.  Decomposing a scene into geometric and semantically consistent regions , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Luc Van Gool,et al.  SEEDS: Superpixels Extracted Via Energy-Driven Sampling , 2012, International Journal of Computer Vision.

[6]  Vincent Mazet,et al.  Automatic rectangular building detection from VHR aerial imagery using shadow and image segmentation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

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

[8]  Xiangyu Zhu,et al.  Object detection by labeling superpixels , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Chen Li,et al.  Stem cell microscopic image segmentation using supervised normalized cuts , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[10]  Luis E. Ortiz,et al.  Parsing clothing in fashion photographs , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Minh N. Do,et al.  Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[14]  Wei-Shi Zheng,et al.  Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[15]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

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

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

[18]  Xiaochun Cao,et al.  Topology Preserved Regular Superpixel , 2012, 2012 IEEE International Conference on Multimedia and Expo.