Parameter-free image segmentation with SLIC

Abstract In this paper, we develop a parameter-free image segmentation framework using Simple Linear Iterative Clustering (SLIC) and Extreme Learning Machines (ELM). SLIC requires a single parameter, the number of centroids k. Our framework, called PF-SLIC (Parameter-Free SLIC) uses an ELM to predict the optimal k, generating a parameter-free framework. PF-SLIC and its streaming variant SPF-SLIC (Streaming PF-SLIC) achieve performance comparable to other models on ultra-high-definition (4K) images and streams, with runtimes orders of magnitude lower.

[1]  Junzhou Huang,et al.  Simplified Labeling Process for Medical Image Segmentation , 2012, MICCAI.

[2]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[3]  D. W. Chinchkhede,et al.  IMAGE SEGMENTATION IN VIDEO SEQUENCES USING MODIFIED BACKGROUND SUBTRACTION , 2012 .

[4]  Jianfei Cai,et al.  Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation , 2015, J. Vis. Commun. Image Represent..

[5]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[6]  Waseem Khan,et al.  Image Segmentation Techniques: A Survey , 2014 .

[7]  Mengjie Zhang,et al.  Image Segmentation: A Survey of Methods Based on Evolutionary Computation , 2014, SEAL.

[8]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

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

[10]  Amaury Lendasse,et al.  High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications , 2015, IEEE Access.

[11]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[12]  Nan Liu,et al.  Voting based extreme learning machine , 2012, Inf. Sci..

[13]  Amaury Lendasse,et al.  Regularized extreme learning machine for regression with missing data , 2013, Neurocomputing.

[14]  Iuri Frosio,et al.  Adaptive Segmentation based on a Learned Quality Metric , 2015, VISAPP.

[15]  Xuelong Li,et al.  DSets-DBSCAN: A Parameter-Free Clustering Algorithm , 2016, IEEE Transactions on Image Processing.

[16]  Hélène Laurent,et al.  Unsupervised evaluation of image segmentation application to multi-spectral images , 2004, ICPR 2004.

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

[18]  Francisco Argüello,et al.  Efficient ELM-Based Techniques for the Classification of Hyperspectral Remote Sensing Images on Commodity GPUs , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Zhi-Zhong Mao,et al.  An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace , 2010, IEEE Transactions on Automation Science and Engineering.

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

[21]  Umar Mohammed,et al.  Superpixel lattices , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Paria Mehrani,et al.  Superpixels and Supervoxels in an Energy Optimization Framework , 2010, ECCV.

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

[24]  Lazaros Mavridis,et al.  PFClust: a novel parameter free clustering algorithm , 2013, BMC Bioinformatics.

[25]  Martin D. Levine,et al.  Low Level Image Segmentation: An Expert System , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[27]  Amaury Lendasse,et al.  Probabilistic Methods for Multiclass Classification Problems , 2016 .

[28]  Zhiming Gui,et al.  Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine , 2014 .

[29]  Milan Sonka,et al.  Image pre-processing , 1993 .

[30]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[31]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

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

[33]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[34]  William B. March,et al.  MLPACK: a scalable C++ machine learning library , 2012, J. Mach. Learn. Res..

[35]  Amaury Lendasse,et al.  TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization , 2011, Neurocomputing.