Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

A deep semantic framework for multimodal representation learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Multimodal representation learning has gained increasing importance in various real-world multimedia applications. Most previous approaches focused on exploring inter-modal correlation by learning a common or intermediate space in a conventional way, e.g. Canonical Correlation Analysis (CCA). These works neglected the exploration of fusing multiple modalities at higher semantic level. In this paper, inspired by the success of deep networks in multimedia computing, we propose a novel unified deep neural framework for multimodal representation learning. To capture the high-level semantic correlations across modalities, we adopted deep learning feature as image representation and topic feature as text representation respectively. In joint model learning, a 5-layer neural network is designed and enforced with a supervised pre-training in the first 3 layers for intra-modal regularization. The extensive experiments on benchmark Wikipedia and MIR Flickr 25K datasets show that our approach achieves state-of-the-art results compare to both shallow and deep models in multimodal and cross-modal retrieval.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. 1 http://www.svcl.ucsd.edu/project/crossmodal/

  2. 2 http://press.liacs.nl/mirflickr/

  3. 3 https://www.flickr.com/

  4. 4 https://github.com/BVLC/caffe/tree/master/models/

  5. 5 The P-R values read from graph

References

  1. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  2. Bengio Y, Lamblin P, Popovici D, Larochelle H et al (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19:153

    Google Scholar 

  3. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  4. Bosch A, Zisserman A, Munoz X (2007) Image classification using random forests and ferns. In: IEEE 11th international conference on computer vision, ICCV 2007. IEEE, pp 1–8

  5. Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75

    Article  MathSciNet  Google Scholar 

  6. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

  7. Escalante HJ, Hérnadez CA, Sucar LE, Montes M (2008) Late fusion of heterogeneous methods for multimedia image retrieval. In: Proceedings of the 1st ACM international conference on multimedia information retrieval. ACM, pp 172–179

  8. Feng F, Wang X, Li R (2014) Cross-modal retrieval with correspondence autoencoder. In: Proceedings of the ACM international conference on multimedia. ACM, pp 7–16

  9. Hinton G, Deng L, Yu D, Dahl GE, Mohamed A-r, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  10. Hoffman J, Rodner E, Donahue J, Darrell T, Saenko K (2013) Efficient learning of domain-invariant image representations. arXiv:1301.3224

  11. Huiskes MJ, Lew MS (2008) The mir flickr retrieval evaluation. In: Proceedings of the 1st ACM international conference on multimedia information retrieval. ACM, pp 39–43

  12. Jacob L, Vert J-p, Francis R Bach. (2009) Clustered multi-task learning: a convex formulation. Adv Neural Inf Process Syst, pp 745–752

  13. Jaderberg M, Vedaldi A, Zisserman A (2014) Deep features for text spotting. In: ECCV. Springer, Berlin, pp 512–528

  14. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. arXiv:1408.5093

  15. Kang C, Xiang S, Liao S, Xu C, Pan C (2015) Learning consistent feature representation for cross-modal multimedia retrieval. IEEE Trans Multimedia 17(3):370–381

    Article  Google Scholar 

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst, pp 1097–1105

  17. Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 609–616

  18. Liao R, Zhu J, Qin Z (2014) Nonparametric bayesian upstream supervised multi-modal topic models. In: Proceedings of the 7th ACM international conference on Web search and data mining. ACM, pp 493– 502

  19. Lienhart R, Romberg S, Hörster E (2009) Multilayer plsa for multimodal image retrieval. In: Proceedings of the ACM international conference on image and video retrieval. ACM, p 9

  20. Liu D, Lai K-T, Ye G, Chen M-S, Chang S-F (2013) Sample-specific late fusion for visual category recognition. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 803–810

  21. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  22. Manjunath BS, Ohm J-R, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715

    Article  Google Scholar 

  23. Mao X, Lin B, Cai D, He X, Pei J (2013) Parallel field alignment for cross media retrieval. In: Proceedings of the 21st ACM international conference on Multimedia. ACM, pp 897–906

  24. Mikolajczyk FYK Deep correlation for matching images and text. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, p 2015

  25. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 689–696

  26. Oliva Aude, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  27. Pereira JC, Coviello E, Doyle G, Rasiwasia N, Lanckriet GRG, Levy R, Vasconcelos N (2014) On the role of correlation and abstraction in cross-modal multimedia retrieval. IEEE Trans Pattern Anal Mach Intell 36(3):521–535

    Article  Google Scholar 

  28. Pereira JC, Vasconcelos N (2014) Cross-modal domain adaptation for text-based regularization of image semantics in image retrieval systems. Comput Vis Image Underst 124:123–135

    Article  Google Scholar 

  29. Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007). IEEE, pp 1–8

  30. Pham T-T, Maillot NE, Lim J-H, Chevallet J-P (2007) Latent semantic fusion model for image retrieval and annotation. In: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. ACM, pp 439–444

  31. Pulla C, Jawahar CV (2010) Multi modal semantic indexing for image retrieval. In: Proceedings of the ACM international conference on image and video retrieval. ACM, pp 342–349

  32. Rasiwasia N, Pereira JC, Coviello E, Doyle G, Lanckriet GRG, Levy R, Vasconcelos N (2010) A new approach to cross-modal multimedia retrieval. In: Proceedings of the international conference on multimedia. ACM, pp 251–260

  33. Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2160–2167

  34. Shu X, Qi G-J, Tang J, Wang J (2015) Weakly-shared deep transfer networks for heterogeneous-domain knowledge propagation. In: Proceedings of the 23rd annual ACM conference on multimedia conference. ACM, pp 35–44

  35. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  36. Srivastava N, Salakhutdinov R (2012) Multimodal learning with deep boltzmann machines. Adv Neural Inf Process Syst, pp 2222–2230

  37. Thompson B (2005) Canonical correlation analysis. Encyclopedia of statistics in behavioral science

  38. Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the international conference on multimedia. ACM, pp 1469–1472

  39. Vincent Pascal, Larochelle Hugo, Lajoie Isabelle, Bengio Yoshua, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  40. Wang K, He R, Wang W, Wang L, Tan T (2013) Learning coupled feature spaces for cross-modal matching. In: IEEE international conference on computer vision (ICCV). IEEE, pp 2088–2095

  41. Wang W, Ooi BC, Yang X, Zhang D, Zhuang Y (2014) Effective multi-modal retrieval based on stacked auto-encoders. Proceedings of the VLDB Endowment 7 (8):649–660

    Article  Google Scholar 

  42. Wang Y, Wu F, Song J, Li X, Zhuang Y (2014) Multi-modal mutual topic reinforce modeling for cross-media retrieval. In: Proceedings of the ACM international conference on multimedia. ACM, pp 307– 316

  43. Wu F, Lu X, Zhang Z, Yan S, Rui Y, Zhuang Y (2013) Cross-media semantic representation via bi-directional learning to rank. In: Proceedings of the 21st ACM international conference on multimedia. ACM, pp 877–886

  44. Wu Z, Jiang Y-G, Wang J, Pu J, Xue X (2014) Exploring inter-feature and inter-class relationships with deep neural networks for video classification. In: Proceedings of the ACM international conference on multimedia. ACM, pp 167–176

  45. Wu F, Zhang Y, Lu WM, Zhuang YT, Wang YF (2013) Supervised coupled dictionary learning with group structures for multi-modal retrieval. In: Twenty-Seventh AAAI Conference on Artificial Intelligence

  46. Xie L, Pan P, Lu Y (2013) A semantic model for cross-modal and multi-modal retrieval. In: Proceedings of the 3rd ACM conference on international conference on multimedia retrieval. ACM, pp 175– 182

  47. Yu J, Cong Y, Qin Z, Wan T (2012) Cross-modal topic correlations for multimedia retrieval. In: Proceedings of the 21st international conference on pattern recognition (ICPR). IEEE, pp 246– 249

  48. Yu Z, Wu F, Yang Y, Tian Q, Luo Jiebo, Zhuang Y (2014) Discriminative coupled dictionary hashing for fast cross-media retrieval. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, pp 395–404

  49. Zhai X, Peng Y, Xiao J (2012) Cross-modality correlation propagation for cross-media retrieval. In: IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2337–2340

  50. Zhai X, Peng Y, Xiao J (2013) Cross-media retrieval by intra-media and inter-media correlation mining. Multimedia Systems 19(5):395–406

    Article  Google Scholar 

  51. Zhang Y, Yeung D-Y (2012) A convex formulation for learning task relationships in multi-task learning. UAI

  52. Zhou J, Ding G, Guo Y (2014) Latent semantic sparse hashing for cross-modal similarity search. In: Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval. ACM, pp 415–424

Download references

Author information

Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany

    Cheng Wang, Haojin Yang & Christoph Meinel

Authors
  1. Cheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haojin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Meinel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Yang, H. & Meinel, C. A deep semantic framework for multimodal representation learning. Multimed Tools Appl 75, 9255–9276 (2016). https://doi.org/10.1007/s11042-016-3380-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3380-8

Keywords

Search

Navigation