交通场景的多视觉特征图像分割方法

doi:10.3969/j.issn.1001-2400.2015.06.003

Abstract

Abstract:

Scene segmentations based on the pixel classifying calculation are complicated, and they use insufficient features, thus resulting in a low accuracy, so a new model is proposed to overcome these shortcomings,which is to learn these geometric classes based on multi-visual features of super-pixels. First, various features are extracted from the super-pixels of an input image. These features are used for classifying the super-pixels. Then the difference between the adjacent super-pixels is calculated to predict their consistency. The initial classification result and the consistency are synthesized to the Markov Random Field energy function, which is then minimized based on the graph-cuts algorithm to get the final labels of the super-pixels. Experimental results prove the effectiveness of the multi-visual features and the optimization method proposed, with superior performance achieved for traffic scenes.

Key words: scene segmentation algorithm, super-pixels, multi-visual feature extraction, random forest regression, Markov random fields

DENG Yanzi;LU Zhaoyang;LI Jing. Segmentation of the image with multi-visual features for a traffic scene[J].J4, 2015, 42(6): 11-16.

References

［1］ Tighe J, Lazebnik S. Superparsing: Scalable Nonparametric Image Parsing with Superpixels［J］. International Journal of Computer Vision, 2013, 101(2): 329-349.
［2］王卫卫, 杨塨鹏, 吕畅, 等. 一种新的水平集图像分割模型［J］. 西安电子科技大学学报, 2013, 40(6): 39-45.
Wang Weiwei, Yang Gongpeng, Lü Chang, et al. New Image Segmentation Model Based on the Level Set Method ［J］. Journal of Xidian University, 2013, 40(6): 39-45.
［3］ Hoiem D, Efros A A, Hebert M. Recovering Surface Layout from an Image［J］. International Journal of Computer Vision, 2007, 75(1): 151-172.
［4］ Ladicky L', Russell C, Kohli P, et al. Inference Methods for CRFs with Co-occurrence Statistics［J］. International Journal of Computer Vision, 2013, 103(2): 213-225.
［5］ Shotton J, Johnson M, Cipolla R. Semantic Texton Forests for Image Categorization and Segmentation［C］//Proceedings of 26th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2008: 1-8.
［6］ He X, Zemel R S, Carreira-Perpindn M A. Multiscale Conditional Random Fields for Image Labeling［C］//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society, 2004: Ⅱ-695-702.
［7］ Gould S, Fulton R, Koller D. Decomposing a Scene into Geometric and Semantically Consistent Regions［C］//Proceedings of the IEEE Conference on Computer Vision. Piscataway: IEEE, 2009: 1-8.
［8］ Galleguillos C, McFee B, Belongie S, et al. Multi-class Object Localization by Combining Local Contextual Interactions［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2010: 113-120.
［9］ Socher R, Lin C C, Manning C D, et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks［C］//Proceedings of the 28th International Conference on Machine Learning. New York: ACM, 2011: 129-136.
［10］ Felzenszwalb P F, Huttenlocher D P. Efficient Graph-based Image Segmentation［J］. International Journal of Computer Vision, 2004, 59(2): 167-181.
［11］ Tighe J, Lazebnik S. Finding Things: Image Parsing with Regions and Per-exemplar Detectors［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington: IEEE Computer Society, 2013: 3001-3008.
［12］ Geiger A, Lauer M, Wojek C, et al. 3D Traffic Scene Understanding from Movable Platforms［J］. Pattern Analysis and Machine Intelligence, 2014, 36(5): 1012-1025.
［13］ Zhao P, Fang T, Xiao J X, et al. Rectilinear Parsing of Architecture in Urban Environment［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2010: 342-349.
［14］ Lowe D G. Distinctive Image Features from Scale-invariant Key Points［J］. International Journal of Computer Vision, 2004, 60(2): 91-110.
［15］ Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection［C］//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society, 2005: 886-893.
［16］ Moosmann F, Triggs B, Jurie F. Fast Discriminative Visual Codebooks Using Randomized Clustering Forests［C］//Advances in Neural Information Processing Systems. Canada: NIPS, 2007: 985-992.
［17］ Wang C, Komodakis N, Paragios N. Markov Random Field Modeling, Inference ＆ Learning in Computer Vision ＆ Image Understanding: a Survey［J］. Computer Vision and Image Understanding, 2013, 117(11): 1610-1627.
［18］ Gridchyn I, Kolmogorov V. Potts Model, Parametric Max-flow and k-sub-modular Functions［C］//Proceedings of the IEEE Conference on Computer Vision. Piscataway: IEEE, 2013: 2320-2327.
［19］ Boykov Y, Veksler O, Zabih R. Fast Approximate Energy Minimization via Graph Cuts［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239.

Segmentation of the image with multi-visual features for a traffic scene

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