Computer Science ›› 2018, Vol. 45 ›› Issue (12): 235-242.doi: 10.11896/j.issn.1002-137X.2018.12.039

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Surface Labeling Method Based on Feed-forward Context and Shape Priors

GUO Yan-fei1, LIU Hong-zhe1, YUAN Jia-zheng1,2, WANG Xue-jiao1   

  1. (Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China)1
    (Beijing Open University,Beijing 100081,China)2
  • Received:2017-11-10 Online:2018-12-15 Published:2019-02-25

Abstract: Aiming at the problem that the scene semantics cannot be understood caused by mutual occlusion,this paper proposed a method based on feed-forward context and shape priors to semantically label the foreground region and the occluded background area.Firstly,the original image is divided into super pixels,and the feature of pixel is extracted.The accelerated decision tree method is used to mark the foreground and the target model is detected by the improved multi-scale deformable component model method.Then,the visible object information is combined with the feed-forward context prediction to infer the occluded portions of background region.Next,the prior knowledge of shape for each label is provided based on polygons which match the current label confidence.Finally,the pixel prediction is combined with the visual plane prediction and the polygon knowledge to form a complete scene labeling image.Compared with the exis-ting method,this method can get more consistent results with the street scene,and can perform better labeling effect when the sidewalk is close to the road.

Key words: Feed-forward context, Multi-scale deformable component model, Scene understanding, Shape priors, Surface labeling

CLC Number: 

  • TP391
[1]SOULY N,SHAH M.Scene labeling using sparse precision matrix[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:3650-3658.
[2]LADICKY′ L,STURGESS P,ALAHARI K,et al.What,where and how many? combining object detectors and crfs[C]∥European conference on computer vision.Springer Berlin Heidelberg,2010:424-437.
[3]LIU C,YUEN J,TORRALBA A.Nonparametric scene parsing:Label transfer via dense scene alignment[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:1972-1979.
[4]TIGHE J,LAZEBNIK S.Superparsing:scalable nonparametric image parsing with superpixels[J].Eruopean Conference on Computer Vision,2010,101(2):352-365.
[5]EIGEN D,FERGUS R.Nonparametric image parsing usingadaptive neighbor sets[J].Computer vision and pattern recognition,2012,157(10):2799-2806.
[6]SINGH G,KOSECKA J.Nonparametric scene parsing withadaptive feature relevance and semantic context[C]∥Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:3151-3157.
[7]LECUN Y,BOSER B,DENKER J S,et al.Backpropagation applied to handwritten zip code recognition[J].Neural Computation,1989,1(4):541-551.
[8]FARABET C,COUPRIE C,NAJMAN L,et al.Learning Hiera-rchical Features for Scene Labeling[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(8):1915-1929.
[9]LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440.
[10]SHUAI B,WANG G,ZUO Z,et al.Integrating parametric and non-parametric models for scene labeling[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:4249-4258.
[11]PINHEIRO P,COLLOBERT R.Recurrent convolutional neural networks for scene labeling[C]∥International Conference on Machine Learning.2014:82-90.
[12]LIANG M,HU X,ZHANG B.Convolutional neural networks with intra-layer recurrent connections for scene labeling[C]∥Advances in Neural Information Processing Systems.2015:937-945.
[13]HOIEM D,EFROS A A,HEBERT M.Recovering Surface La-yout from an Image[J].International Journal of Computer Vision,2007,75(1):151-172.
[14]FZLZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Ojbect Detection with Discriminatively Trained Part-Based Models.IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(9):1627-1645.
[15]GRIBBON K T,BAILEY D G.A novel approach to real-time bilinear interpolation[C]∥Second IEEE International Workshop on Electronic Design.IEEE,2004:126-131.
[16]HWANG J W,LEE H S.Adaptive image interpolation based on local gradient features[J].IEEE Signal Processing Letters,2004,11(3):359-362.
[17]TU Z.Auto-context and its application to high-level vision tasks[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2008(CVPR 2008).IEEE,2008:1-8.
[18]SHOTTON J,JOHNSON M,CIPOLLA R.Semantic texton fore-sts for image categorization and segmentation[C]∥IEEE Conference on Computer Vision & Pattern Recognition.2008:1-8.
[19]ZHANG H,XIAO J,QUAN L.Supervised Label Transfer for Semantic Segmentation of Street Scenes∥European Confer-ence on Computer Vision.2010:561-574.
[20]BYEON W,BREUEL T M,RAUE F,et al.Scene labeling with LSTM recurrent neural networks[C]∥Computer Vision and Pattern Recognition.IEEE,2015:3547-3555.
[21]TIGHE J,LAZEBNIK S.Finding things:Image parsing with re-gions and per-exemplar detectors[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2013:3001-3008.
[1] YAO Tuo-zhong, ZUO Wen-hui, AN Peng, SONG Jia-tao. Multi-semantic Interaction Based Iterative Scene Understanding Framework [J]. Computer Science, 2019, 46(5): 228-234.
[2] LIANG Hao-zhe , LI Guo-hui, ZHANG Jun. Surveillance Scene Analysis Model Based on Motion Trajectory [J]. Computer Science, 2011, 38(9): 264-266.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!