Conceptual space based model fitting for multi-structure data

Abstract In this paper, we propose a novel fitting method, called the Conceptual Space based Model Fitting (CSMF), to fit and segment multi-structure data contaminated with a large number of outliers. CSMF includes two main parts: an outlier removal algorithm and a model selection algorithm. Specifically, we firstly construct a novel conceptual space to measure data points by only considering the good model hypotheses. Then we analyze the conceptual space to effectively remove the gross outliers. Based on the results of outlier removal, we propose to search center points (representing the estimated model instances) in the conceptual space for model selection. CSMF is able to efficiently and effectively remove gross outliers in data, and simultaneously estimate the number and the parameters of model instances without using prior information. Experimental results on both synthetic data and real images demonstrate the advantages of the proposed method over several state-of-the-art fitting methods.

[1]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[2]  Christopher Zach,et al.  The Likelihood-Ratio Test and Efficient Robust Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[3]  Yan Yan,et al.  A unified hypothesis generation framework for multi-structure model fitting , 2017, Neurocomputing.

[4]  Tat-Jun Chin,et al.  Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jianping Fan,et al.  iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning , 2017, IEEE Transactions on Information Forensics and Security.

[6]  David Suter,et al.  Hypergraph Modelling for Geometric Model Fitting , 2016, Pattern Recognit..

[7]  Gerard Medioni,et al.  StaRSaC: Stable random sample consensus for parameter estimation , 2009, CVPR.

[8]  Andrea Fusiello,et al.  Robust Multiple Structures Estimation with J-Linkage , 2008, ECCV.

[9]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[10]  Andrea Fusiello,et al.  Fitting Multiple Models via Density Analysis in Tanimoto Space , 2015, ICIAP.

[11]  Yan Yan,et al.  Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data , 2016, ECCV.

[12]  Tat-Jun Chin,et al.  Dynamic and hierarchical multi-structure geometric model fitting , 2011, 2011 International Conference on Computer Vision.

[13]  Fan Zhou,et al.  Method for fundamental matrix estimation combined with feature lines , 2015, Neurocomputing.

[14]  Yan Yan,et al.  Conceptual space based gross outlier removal for geometric model fitting , 2016, 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[15]  Khosrow Dehnad,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[16]  Frank Dellaert,et al.  GroupSAC: Efficient consensus in the presence of groupings , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Anton Osokin,et al.  Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Gaurav S. Sukhatme,et al.  Detecting Moving Objects using a Single Camera on a Mobile Robot in an Outdoor Environment , 2004 .

[19]  Andrea Fusiello,et al.  Multiple Models Fitting as a Set Coverage Problem , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Dacheng Tao,et al.  Multi-View Intact Space Learning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Xiaoyang Tan,et al.  Local subspace smoothness alignment for constrained local model fitting , 2016, Neurocomputing.

[22]  Fan Xiao,et al.  A novel robust model fitting approach towards multiple-structure data segmentation , 2017, Neurocomputing.

[23]  Peter Meer,et al.  Generalized Projection-Based M-Estimator , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Jun Yu,et al.  Click Prediction for Web Image Reranking Using Multimodal Sparse Coding , 2014, IEEE Transactions on Image Processing.

[25]  Yan Yan,et al.  Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[26]  Tat-Jun Chin,et al.  Interacting Geometric Priors For Robust Multimodel Fitting , 2014, IEEE Transactions on Image Processing.

[27]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[28]  Dacheng Tao,et al.  Robust Extreme Multi-label Learning , 2016, KDD.

[29]  Tat-Jun Chin,et al.  Robust fitting of multiple structures: The statistical learning approach , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Jiri Matas,et al.  Optimal Randomized RANSAC , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Shaodi You,et al.  Think locally, fit globally: Robust and fast 3D shape matching via adaptive algebraic fitting , 2017, Neurocomputing.

[32]  Fei Gao,et al.  Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking , 2017, IEEE Transactions on Cybernetics.

[33]  Meng Wang,et al.  Multimodal Deep Autoencoder for Human Pose Recovery , 2015, IEEE Transactions on Image Processing.

[34]  Jiri Matas,et al.  Locally Optimized RANSAC , 2003, DAGM-Symposium.

[35]  Alexander M. Bronstein,et al.  Inverting RANSAC: Global model detection via inlier rate estimation , 2015, CVPR.

[36]  Charles V. Stewart,et al.  Bias in robust estimation caused by discontinuities and multiple structures , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Yuri Boykov,et al.  Energy-Based Geometric Multi-model Fitting , 2012, International Journal of Computer Vision.

[38]  Jana Kosecka,et al.  Nonparametric Estimation of Multiple Structures with Outliers , 2006, WDV.

[39]  Andrea Fusiello,et al.  T-Linkage: A Continuous Relaxation of J-Linkage for Multi-model Fitting , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.