Advertisement

Frontiers of Computer Science

, Volume 12, Issue 5, pp 840–857 | Cite as

Learning deep representations for semantic image parsing: a comprehensive overview

  • Lili Huang
  • Jiefeng Peng
  • Ruimao Zhang
  • Guanbin Li
  • Liang Lin
Research Article

Abstract

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.

Keywords

semantic image segmentation deep learning convolutional neural networks image parsing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This work was supported by the National Science Fund for Excellent Young Scholars (61622214), the National Natural Science Foundation of China (Grant Nos. 61702565 and 61622214), Guangdong Natural Science Foundation Project for Research Teams (2017A030312006), and was also sponsored by CCF-Tencent Open Research Fund.

Supplementary material

11704_2018_7195_MOESM1_ESM.ppt (599 kb)
Supplementary material, approximately 600 KB.

References

  1. 1.
    Zhao H S, Shi J P, Qi X J, Wang X G, Jia J Y. Pyramid scene parsing network. In: Proceedings of International Conference on Computer Vision and Pattern Recognition. 2017, 2881–2890Google Scholar
  2. 2.
    He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. In: Proceedings of IEEE International Conference on Computation Vision. 2017, 2980–2988Google Scholar
  3. 3.
    Tu Z, Chen X, Yuille A L, Zhu S C. Image parsing: unifying segmentation, detection, and recognition. International Journal of Computer Vision, 2005, 63(2): 113–140Google Scholar
  4. 4.
    Tu Z, Zhu S C. Parsing images into region and curve processes. In: Proceedings of European Conference on Computer Vision. 2002, 393–407Google Scholar
  5. 5.
    Han F, Zhu S C. Bottom-up/top-down image parsing with attribute grammar. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1): 59–73Google Scholar
  6. 6.
    Lin L, Wang G, Zhang R, Zhang R, Liang X, Zuo W. Deep structured scene parsing by learning with image descriptions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 2276–2284Google Scholar
  7. 7.
    Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110MathSciNetGoogle Scholar
  8. 8.
    Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 886–893Google Scholar
  9. 9.
    Ahonen T, Hadid A, Pietikäinen M. Face recognition with local binary patterns. In: Proceedings of European Conference on Computer Vision. 2004, 469–481Google Scholar
  10. 10.
    Liu Z, Li X, Luo P, Loy C C, Tang X. Semantic image segmentation via deep parsing network. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 1377–1385Google Scholar
  11. 11.
    Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834–848Google Scholar
  12. 12.
    Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 3431–3440Google Scholar
  13. 13.
    Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. Semantic image segmentation with deep convolutional nets and fully connected crfs. 2014, arXiv preprint arXiv:14127062Google Scholar
  14. 14.
    Peng C, Zhang X, Yu G, Luo G, Sun J. Large kernel matters-improve semantic segmentation by global convolutional network. 2017, arXiv preprint arXiv:170302719Google Scholar
  15. 15.
    Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems. 2012, 1097–1105Google Scholar
  16. 16.
    Socher R, Manning C D, Ng A Y. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In: Proceedings of NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop. 2010, 1–9Google Scholar
  17. 17.
    Li Y, Qi H, Dai J, Ji X, Wei Y. Fully convolutional instance-aware semantic segmentation. 2016, arXiv preprint arXiv:161107709Google Scholar
  18. 18.
    Bengio Y. Deep learning of representations: looking forward. In: Proceedings of International Conference on Statistical Language and Speech Processing. 2013, 1–37Google Scholar
  19. 19.
    Bengio Y. Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning. 2012, 17–36Google Scholar
  20. 20.
    Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798–1828Google Scholar
  21. 21.
    LeCun Y, Boser B, Denker J S, Henderson D, Howard R E, Hubbard W, Jackel L D. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1989, 1(4): 541–551Google Scholar
  22. 22.
    Dai J, He K, Li Y, Ren S, Sun J. Instance-sensitive fully convolutional networks. In: Proceedings of European Conference on Computer Vision. 2016, 534–549Google Scholar
  23. 23.
    Islam M A, Naha S, Rochan M, Bruce N, Wang Y. Label refinement network for coarse-to-fine semantic segmentation. 2017, arXiv preprint arXiv:170300551Google Scholar
  24. 24.
    Lipton Z C, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. 2015, arXiv preprint arXiv:150600019Google Scholar
  25. 25.
    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436–444Google Scholar
  26. 26.
    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 IEEE Conference on Computer Vision and Pattern Recognition. 2016, 3185–3193Google Scholar
  27. 27.
    Karpathy A, Li F F. Deep visual-semantic alignments for generating image descriptions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 3128–3137Google Scholar
  28. 28.
    Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In: Proceedings of Advances in Neural Information Processing Systems. 2014, 3104–3112Google Scholar
  29. 29.
    Li Z, Gan Y, Liang X, Yu Y, Cheng H, Lin L. LSTM-CF: unifying context modeling and fusion with LSTMS for RGB-D scene labeling. In: Proceedings of European Conference on Computer Vision. 2016, 541–557Google Scholar
  30. 30.
    Peng Z, Zhang R, Liang X, Liu X, Lin L. Geometric scene parsing with hierarchical LSTM. 2016, arXiv preprint arXiv:160401931Google Scholar
  31. 31.
    Byeon W, Breuel TM, Raue F, Liwicki M. Scene labeling with LSTM recurrent neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 3547–3555Google Scholar
  32. 32.
    Liang X, Shen X, Feng J, Lin L, Yan S. Semantic object parsing with graph LSTM. In: Proceedings of European Conference on Computer Vision. 2016, 125–143Google Scholar
  33. 33.
    Liang X, Lin L, Shen X, Feng J, Yan S, Xing E P. Interpretable structure-evolving LSTM. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 2175–2184Google Scholar
  34. 34.
    Zhang R, Yang W, Peng Z, Wang X, Lin L. Progressively diffused networks for semantic image segmentation. 2017, arXiv preprint arXiv:170205839Google Scholar
  35. 35.
    Elman J L. Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 1991, 7(2-3): 195–225Google Scholar
  36. 36.
    Liu W, Rabinovich A, Berg A C. Parsenet: looking wider to see better. 2015, arXiv preprint arXiv:150604579Google Scholar
  37. 37.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 1–9Google Scholar
  38. 38.
    He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 770–778Google Scholar
  39. 39.
    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014, arXiv preprint arXiv:14091556Google Scholar
  40. 40.
    Pinheiro P H O, Collobert R. Recurrent convolutional neural networks for scene labeling. In: Proceedings of International Conference on Machine Learning. 2014, 82–90Google Scholar
  41. 41.
    Graves A, Fernández S, Schmidhuber J. Multi-dimensional recurrent neural networks. In: Proceedings of the International Conference on Artificial Neural Networks. 2007, 549–558Google Scholar
  42. 42.
    Lin L, Huang L, Chen T, Gan Y, Cheng H. Knowledge-guided recurrent neural network learning for task-oriented action prediction. In: Proceedings of IEEE International Conference on Multimedia and Expo. 2017, 625–630Google Scholar
  43. 43.
    Farabet C, Couprie C, Najman L, LeCun Y. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915–1929Google Scholar
  44. 44.
    Gupta S, Girshick R, Arbeláez P, Malik J. Learning rich features from RGB-D images for object detection and segmentation. In: Proceedings of European Conference on Computer Vision. 2014, 345–360Google Scholar
  45. 45.
    Ning F, Delhomme D, LeCun Y, Piano F, Bottou L, Barbano P E. Toward automatic phenotyping of developing embryos from videos. IEEE Transactions on Image Processing, 2005, 14(9): 1360–1371Google Scholar
  46. 46.
    Liang X, Liu S, Shen X, Yang J, Liu L, Dong J, Lin L, Yan S. Deep human parsing with active template regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(12): 2402–2414Google Scholar
  47. 47.
    Liang X, Xu C, Shen X, Yang J, Liu S, Tang J, Lin L, Yan S. Human parsing with contextualized convolutional neural network. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 1386–1394Google Scholar
  48. 48.
    Krähenbühl P, Koltun V. Efficientcient inference in fully connected CRFS with gaussian edge potentials. In: Proceedings of Advances in Neural Information Processing Systems. 2011, 109–117Google Scholar
  49. 49.
    Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 1520–1528Google Scholar
  50. 50.
    Badrinarayanan V, Handa A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. 2015, arXiv preprint arXiv:150507293Google Scholar
  51. 51.
    Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. 2015, 234–241Google Scholar
  52. 52.
    Lin G, Milan A, Shen C, Reid I. Refinenet: multi-path refinement networks with identity mappings for high-resolution semantic segmentation. 2016, arXiv preprint arXiv:161106612Google Scholar
  53. 53.
    Chen L C, Papandreou G, Schroff F, Adam H. Rethinking atrous convolution for semantic image segmentation. 2017, arXiv preprint arXiv:170605587Google Scholar
  54. 54.
    Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. 2015, arXiv preprint arXiv:151107122Google Scholar
  55. 55.
    Li X, Liu Z, Luo P, Loy C C, Tang X. Not all pixels are equal: difficulty-aware semantic segmentation via deep layer cascade. 2017, arXiv preprint arXiv:170401344Google Scholar
  56. 56.
    Zhou Y, Xie L, Shen W, Wang Y, Fishman E K, Yuille A L. A fixedpoint model for pancreas segmentation in abdominal ct scans. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. 2017, 693–701Google Scholar
  57. 57.
    Li Q, Wang J, Wipf D, Tu Z. Fixed-point model for structured labeling. In: Proceedings of International Conference on Machine Learning. 2013, 214–221Google Scholar
  58. 58.
    Wang G, Luo P, Lin L, Wang X. Learning object interactions and descriptions for semantic image segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 5859–5867Google Scholar
  59. 59.
    Luo P, Wang G, Lin L, Wang X. Deep dual learning for semantic image segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 2718–2726Google Scholar
  60. 60.
    Schwing A G, Urtasun R. Fully connected deep structured networks. 2015, arXiv preprint arXiv:150302351Google Scholar
  61. 61.
    Yang W, Luo P, Lin L. Clothing co-parsing by joint image segmentation and labeling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3182–3189Google Scholar
  62. 62.
    Byeon W, Liwicki M, Breuel T M. Texture classification using 2D LSTM networks. In: Proceedings of International Conference on Pattern Recognition. 2014, 1144–1149Google Scholar
  63. 63.
    Eigen D, Fergus R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 2650–2658Google Scholar
  64. 64.
    Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 580–587Google Scholar
  65. 65.
    Reza M, Kosecka J. Reinforcement learning for semantic segmentation in indoor scenes. 2016, arXiv preprint arXiv:160601178Google Scholar
  66. 66.
    Van Oord A, Kalchbrenner N, Kavukcuoglu K. Pixel recurrent neural networks. In: Proceedings of International Conference on Machine Learning. 2016, 1747–1756Google Scholar
  67. 67.
    Kalchbrenner N, Danihelka I, Graves A. Grid long short-term memory. 2015, arXiv preprint arXiv:150701526Google Scholar
  68. 68.
    Hariharan B, Arbeláez P, Girshick R, Malik J. Simultaneous detection and segmentation. In: Proceedings of European Conference on Computer Vision. 2014, 297–312Google Scholar
  69. 69.
    Liang X, Wei Y, Shen X, Jie Z, Feng J, Lin L, Yan S. Reversible recursive instance-level object segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 633–641Google Scholar
  70. 70.
    Liang X, Wei Y, Shen X, Yang J, Lin L, Yan S. Proposal-free network for instance-level object segmentation. 2015, arXiv preprint arXiv:150902636Google Scholar
  71. 71.
    Abtahi F, Zhu Z, Burry AM. A deep reinforcement learning approach to character segmentation of license plate images. In: Proceedings of International Conference on Machine Vision Applications. 2015, 539–542Google Scholar
  72. 72.
    Lin L, Wang K, Zuo W, Wang M, Luo J, Zhang L. A deep structured model with radius–margin bound for 3D human activity recognition. International Journal of Computer Vision, 2016, 118(2): 256–273MathSciNetGoogle Scholar
  73. 73.
    Hariharan B, Arbeláez P, Girshick R, Malik J. Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 447–456Google Scholar
  74. 74.
    Chen Y T, Liu X, Yang M H. Multi-instance object segmentation with occlusion handling. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 3470–3478Google Scholar
  75. 75.
    Arbeláez P, Pont-Tuset J, Barron J T, Marques F, Malik J. Multiscale combinatorial grouping. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 328–335Google Scholar
  76. 76.
    Li G, Xie Y, Lin L, Yu Y. Instance-level salient object segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 247–256Google Scholar
  77. 77.
    Dai J, He K, Sun J. Instance-aware semantic segmentation via multitask network cascades. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 3150–3158Google Scholar
  78. 78.
    Girshick R. Fast r-cnn. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 1440–1448Google Scholar
  79. 79.
    He K, Zhang X, Ren S, Sun J. Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Proceedings of European Conference on Computer Vision. 2014, 346–361Google Scholar
  80. 80.
    Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of Advances in Neural Information Processing Systems. 2015, 91–99Google Scholar
  81. 81.
    Newell A, Huang Z, Deng J. Associative embedding: end-to-end learning for joint detection and grouping. In: Proceedings of Advances in Neural Information Processing Systems. 2017, 2274–2284Google Scholar
  82. 82.
    Harley A W, Derpanis K G, Kokkinos I. Learning dense convolutional embeddings for semantic segmentation. 2015, arXiv preprint arXiv:151104377Google Scholar
  83. 83.
    Fathi A, Wojna Z, Rathod V, Wang P, Song H O, Guadarrama S, Murphy K P. Semantic instance segmentation via deep metric learning. 2017, arXiv preprint arXiv:170310277Google Scholar
  84. 84.
    Yang L, Jin R. Distance metric learning: a comprehensive survey. Michigan State Universiy, 2006, 2(2): 1–51MathSciNetGoogle Scholar
  85. 85.
    Xu J, Schwing A G, Urtasun R. Tell me what you see and I will show you where it is. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 3190–3197Google Scholar
  86. 86.
    Miller G A, Beckwith R, Fellbaum C, Gross D, Miller K J. Introduction to wordnet: an on-line lexical database. International Journal of Lexicography, 1990, 3(4): 235–244Google Scholar
  87. 87.
    Socher R, Bauer J, Manning C D, Ng A Y. Parsing with compositional vector grammars. In: Proceedings of Annual Meeting of the Association for Computational Linguistics. 2013, 455–465Google Scholar
  88. 88.
    Everingham M, Van Gool L, Williams C K, Winn J, Zisserman A. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 2010, 88(2): 303–338Google Scholar
  89. 89.
    Chen X, Mottaghi R, Liu X, Fidler S, Urtasun R, Yuille A L. Detect what you can: detecting and representing objects using holistic models and body parts. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1971–1978Google Scholar
  90. 90.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115(3): 211–252MathSciNetGoogle Scholar
  91. 91.
    Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A. Semantic understanding of scenes through the ADE20K dataset. 2016, arXiv preprint arXiv:160805442Google Scholar
  92. 92.
    Lin T Y, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick C L, Dollár P. Microsoft COCO: common objects in context. 2015, arXiv preprint arXiv:14050312v3Google Scholar
  93. 93.
    Lin T Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick C L. Microsoft COCO: common objects in context. In: Proceedings of European Conference on Computer Vision. 2014, 740–755Google Scholar
  94. 94.
    Liu C, Yuen J, Torralba A. Nonparametric scene parsing: label transfer via dense scene alignment. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1972–1979Google Scholar
  95. 95.
    Silberman N, Hoiem D, Kohli P, Fergus R. Indoor segmentation and support inference from RGBD images. In: Proceedings of European Conference on Computer Vision. 2012, 746–760Google Scholar
  96. 96.
    Gupta S, Arbelaez P, Malik J. Perceptual organization and recognition of indoor scenes from RGB-D images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 564–571Google Scholar
  97. 97.
    Song S, Lichtenberg S P, Xiao J. Sun RGB-D: a RGB-D scene understanding benchmark suite. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2015, 567–576Google Scholar
  98. 98.
    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. Consumer Depth Cameras for Computer Vision. London: Springer, 2013Google Scholar
  99. 99.
    Xiao J, Owens A, Torralba A. SUN3D: a database of big spaces reconstructed using SFM and object labels. In: Proceedings of IEEE International Conference on Computer Vision. 2013, 1625–1632Google Scholar
  100. 100.
    Yamaguchi K, Kiapour M H, Ortiz L E, Berg T L. Parsing clothing in fashion photographs. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 3570–3577Google Scholar
  101. 101.
    Liu S, Feng J, Domokos C, Xu H, Huang J, Hu Z, Yan S. Fashion parsing with weak color-category labels. IEEE Transactions on Multimedia, 2014, 16(1): 253–265Google Scholar
  102. 102.
    Dong J, Chen Q, XiaW, Huang Z, Yan S. A deformable mixture parsing model with parselets. In: Proceedings of IEEE International Conference on Computer Vision. 2013, 3408–3415Google Scholar
  103. 103.
    Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B. The cityscapes dataset for semantic urban scene understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2016, 3213–3223Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Lili Huang
    • 1
  • Jiefeng Peng
    • 1
  • Ruimao Zhang
    • 1
  • Guanbin Li
    • 1
  • Liang Lin
    • 1
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

Personalised recommendations