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LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

  • Zhen Li
  • Yukang Gan
  • Xiaodan Liang
  • Yizhou Yu
  • Hui Cheng
  • Liang LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9906)

Abstract

Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by (i) developing a novel Long Short-Term Memorized Context Fusion (LSTM-CF) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and (ii) incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training. Specifically, contexts in photometric and depth channels are, respectively, captured by stacking several convolutional layers and a long short-term memory layer; the memory layer encodes both short-range and long-range spatial dependencies in an image along the vertical direction. Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts. At last, the fused contextual representation is concatenated with the convolutional features extracted from the photometric channels in order to improve the accuracy of fine-scale semantic labeling. Our proposed model has set a new state of the art, i.e., \({\mathbf{48.1}}\%\) and \({\mathbf{49.4}}\%\) average class accuracy over 37 categories (\({\mathbf{2.2}}\%\) and \({\mathbf{5.4}}\%\) improvement) on the large-scale SUNRGBD dataset and the NYUDv2 dataset, respectively.

Keywords

RGB-D scene labeling Image context modeling Long short-term memory Depth and photometric data fusion 

References

  1. 1.
    Wu, C., Lenz, I., Saxena, A.: Hierarchical semantic labeling for task-relevant RGB-D perception. In: Robotics: Science and Systems (RSS) (2014)Google Scholar
  2. 2.
    Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013)Google Scholar
  3. 3.
    Holz, D., Holzer, S., Rusu, R.B., Behnke, S.: Real-time plane segmentation using RGB-D cameras. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds.) RoboCup 2011. LNCS, vol. 7416, pp. 306–317. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., Manning, C.D.: Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In: Proceedings of the Fourth Workshop on Vision and Language, pp. 70–80 (2015)Google Scholar
  5. 5.
    Yan, Z., Zhang, H., Wang, B., Paris, S., Yu, Y.: Automatic photo adjustment using deep neural networks. ACM Trans. Graph. 35(2), 11 (2016)CrossRefGoogle Scholar
  6. 6.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)CrossRefGoogle Scholar
  7. 7.
    Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1–8. IEEE (2009)Google Scholar
  8. 8.
    Tighe, J., Lazebnik, S.: SuperParsing: scalable nonparametric image parsing with superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Khan, S.H., Bennamoun, M., Sohel, F., Togneri, R., Naseem, I.: Integrating geometrical context for semantic labeling of indoor scenes using RGBD images. Int. J. Comput. Vis. 117, 1–20 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  11. 11.
    Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: Conditional random fields as recurrent neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1529–1537 (2015)Google Scholar
  12. 12.
    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv preprint arXiv:1412.7062 (2014)
  13. 13.
    Gupta, S., Girshick, R., Arbeláez, P., Malik, J.: Learning rich features from RGB-D images for object detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 345–360. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Song, S., Xiao, J.: Deep sliding shapes for amodal 3D object detection in RGB-D images. arXiv preprint arXiv:1511.02300 (2015)
  15. 15.
    Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: Semantic image segmentation via deep parsing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1377–1385 (2015)Google Scholar
  16. 16.
    Liang, X., Shen, X., Xiang, D., Feng, J., Lin, L., Yan, S.: Semantic object parsing with local-global long short-term memory. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  17. 17.
    Byeon, W., Breuel, T.M., Raue, F., Liwicki, M.: Scene labeling with LSTM recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3547–3555 (2015)Google Scholar
  18. 18.
    Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene labeling. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 82–90 (2014)Google Scholar
  19. 19.
    Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A., Bengio, Y.: Renet: a recurrent neural network based alternative to convolutional networks. arXiv preprint arXiv:1505.00393 (2015)
  20. 20.
    Song, S., Lichtenberg, S.P., Xiao, J.: Sun RGB-D: a RGB-D scene understanding benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 567–576 (2015)Google Scholar
  21. 21.
    Kumar, M.P., Koller, D.: Efficiently selecting regions for scene understanding. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3217–3224. IEEE (2010)Google Scholar
  22. 22.
    Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)
  23. 23.
    Lempitsky, V., Vedaldi, A., Zisserman, A.: Pylon model for semantic segmentation. In: Advances in Neural Information Processing Systems, pp. 1485–1493 (2011)Google Scholar
  24. 24.
    Ren, X., Bo, L., Fox, D.: RGB-(D) scene labeling: features and algorithms. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2759–2766. IEEE (2012)Google Scholar
  25. 25.
    Gupta, S., Arbeláez, P., Girshick, R., Malik, J.: Indoor scene understanding with RGB-D images: bottom-up segmentation, object detection and semantic segmentation. Int. J. Comput. Vis. 112(2), 133–149 (2015)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wang, A., Lu, J., Cai, J., Wang, G., Cham, T.J.: Unsupervised joint feature learning and encoding for RGB-D scene labeling. IEEE Trans. Image Process. 24(11), 4459–4473 (2015)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Husain, F., Schulz, H., Dellen, B., Torras, C., Behnke, S.: Combining semantic and geometric features for object class segmentation of indoor scenes. IEEE Rob. Autom. Lett. 2(1), 49–55 (2017)CrossRefGoogle Scholar
  28. 28.
    Schmidhuber, J.: A local learning algorithm for dynamic feedforward and recurrent networks. Connection Sci. 1(4), 403–412 (1989)CrossRefGoogle Scholar
  29. 29.
    Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)CrossRefGoogle Scholar
  30. 30.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  31. 31.
    Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 545–552 (2009)Google Scholar
  32. 32.
    Stollenga, M.F., Byeon, W., Liwicki, M., Schmidhuber, J.: Parallel multi-dimensional LSTM, with application to fast biomedical volumetric image segmentation. In: Advances in Neural Information Processing Systems, pp. 2980–2988 (2015)Google Scholar
  33. 33.
    Li, G., Yu, Y.: Deep contrast learning for salient object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  34. 34.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)Google Scholar
  35. 35.
    Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K., Darrell, T.: A category-level 3D object dataset: putting the kinect to work. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer Depth Cameras for Computer Vision, pp. 141–165. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  36. 36.
    Xiao, J., Owens, A., Torralba, A.: SUN3D: a database of big spaces reconstructed using SfM and object labels. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1625–1632 (2013)Google Scholar
  37. 37.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  38. 38.
    Liu, C., Yuen, J., Torralba, A.: Sift flow: dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 978–994 (2011)CrossRefGoogle Scholar
  39. 39.
    Couprie, C., Farabet, C., Najman, L., LeCun, Y.: Toward real-time indoor semantic segmentation using depth information. J. Mach. Learn. Res. (2014)Google Scholar
  40. 40.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zhen Li
    • 1
  • Yukang Gan
    • 2
  • Xiaodan Liang
    • 2
  • Yizhou Yu
    • 1
  • Hui Cheng
    • 2
  • Liang Lin
    • 2
    Email author
  1. 1.Department of Computer ScienceThe University of Hong KongHong KongChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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