|
|
|
| Multi-view Clustering Based Natural Image Contour Detection |
| ZHANG Heng, TAN Xiaoyang, JIN Xin |
| College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 |
|
|
|
|
Abstract The gradient feature gives an invariant description for linear lighting changes while sparse coding methods can exploit the data statistics from the image data point. In multi-view clustering algorithm, different attributes set in the same cluster are considered as different views, and the importance of different views is taken into account for co-clustering. An algorithm based on multi-view clustering for image contour detection is proposed and it integrates both features into a unified multi-view clustering framework to effectively improve the robustness of the detection system. The combination of image local features and sparse code features is utilized to train model, and the spatial information and curvature information of the image pixels are added to obtain the global features and ensure the accuracy of the contour detection and region consistency. Experiments on two large public available datasets show the feasibility and effectiveness of the proposed algorithm.
|
|
Received: 12 May 2015
|
|
|
|
|
|
[1] ARBELAEZ P, MAIRE M, FOWLKES C, et al. Contour Detection and Hierarchical Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898-916.
[2] SILBERMAN N, FERGUS R. Indoor Scene Segmentation Using a Structured Light Sensor // Proc of the IEEE International Confe-rence on Computer Vision Workshops. Barcelona, Spain, 2011: 601-608.
[3] CANNY J. A Computational Approach to Edge Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6): 679-698.
[4] SHI J, MALIK J. Normalized Cuts and Image Segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
[5] TREISMAN A, SOUTHER J. Search Asymmetry: A Diagnostic for Preattentive Processing of Separable Features. Journal of Experimental Psychology: General, 1985, 114(3): 285-310.
[6] HUANG J B, MENQ C H. Automatic Data Segmentation for Geometric Feature Extraction from Unorganized 3-D Coordinate Points. IEEE Trans on Robotics and Automation, 2001, 17(3): 268-279.
[7] MARTIN D R, FOWLKES C C, MALIK J. Learning to Detect Na-tural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(5): 530-549.
[8] REN X F. Multi-scale Improves Boundary Detection in Natural Images // Proc of the 10th European Conference on Computer Vision. Marseille, France, 2008, III: 533-545.
[9] MAIRAL J, LEORDEANU M, BACH F, et al. Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation // Proc of the 10th European Conference on Computer Vision. Marseille, France, 2008, III: 43-56.
[10] REN X F, Bo L F. Discriminatively Trained Sparse Code Gradients for Contour Detection // PEREIRA F, BURGES C J C, BOTTOU L, et al., eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2012, 25: 584-592.
[11] DOLLAR P, ZITNICK C L. Structured Forests for Fast Edge Detection[EB/OL]. [2015-02-14]. http://vision.ucsd.edu/~pdollar/files/papers/DollarICCV13edges.pdf.
[12] CAI X, NIE F P, HUANG H. Multi-view K-means Clustering on Big Data // Proc of the 23rd International Joint Conference on Artificial Intelligence. Beijing, China, 2013: 2598-2604.
[13] NIXON M S, AGUADO A S. Feature Extraction & Image Processing. 2nd Edition. Salt Lake City, USA: Academic Press, 2008.
[14] SULAIMAN S N, ISA N A M. Adaptive Fuzzy-K-means Clustering Algorithm for Image Segmentation. IEEE Trans on Consumer Electronics, 2010, 56(4): 2661-2668.
[15] ARBELAEZ P, PONT-TUSET J, Barron J, et al. Multiscale Combinatorial Grouping // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 328-335. |
|
|
|
2016, Vol. 29 
