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Multimedia Tools and Applications

, Volume 77, Issue 22, pp 29435–29455 | Cite as

Irrelevance reduction with locality-sensitive hash learning for efficient cross-media retrieval

  • Yuhua Jia
  • Liang Bai
  • Peng WangEmail author
  • Jinlin Guo
  • Yuxiang Xie
  • Tianyuan Yu
Article
  • 152 Downloads

Abstract

Cross-media retrieval is an imperative approach to handle the explosive growth of multimodal data on the web. However, existing approaches to cross-media retrieval are computationally expensive due to high dimensionality. To efficiently retrieve in multimodal data, it is essential to reduce the proportion of irrelevant documents. In this paper, we propose a fast cross-media retrieval approach (FCMR) based on locality-sensitive hashing (LSH) and neural networks. One modality of multimodal information is projected by LSH algorithm to cluster similar objects into the same hash bucket and dissimilar objects into different ones and then another modality is mapped into these hash buckets using hash functions learned through neural networks. Once given a textual or visual query, it can be efficiently mapped to a hash bucket in which objects stored can be near neighbors of this query. Experimental results show that, in the set of the queries’ near neighbors obtained by the proposed method, the proportions of relevant documents can be much boosted, and it indicates that the retrieval based on near neighbors can be effectively conducted. Further evaluations on two public datasets demonstrate the efficacy of the proposed retrieval method compared to the baselines.

Keywords

Cross-media retrieval Neural networks Locality-sensitive hashing Multimodal indexing 

Notes

Acknowledgments

This work is supported by the Natural Science Foundation of China under Grant No. 61571453, No. 61502264, and No. 61405252, Natural Science Foundation of Hunan Province, China under Grant No. 14JJ3010, Research Funding of National University of Defense Technology under grant No. ZK16-03-37.

References

  1. 1.
    Bengio Y (2009) Learning deep architectures for AI. Founda Trends Mach Learn 2(1):1–127CrossRefGoogle Scholar
  2. 2.
    Bian J, Yang Y, Zhang H et al (2015) Multimedia summarization for social events in microblog stream. IEEE Trans Multimed 17(2):216–228CrossRefGoogle Scholar
  3. 3.
    Blei DM, Jordan MI (2003) Modeling annotated data. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval. ACM, pp 127–134Google Scholar
  4. 4.
    Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation[J]. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  5. 5.
    Chen Z, Samarabandu J, Rodrigo R (2012) Recent advances in simultaneous localization and map-building using computer vision. Adv Robot 21(3-4):233–265CrossRefGoogle Scholar
  6. 6.
    Dong J, Li X, Snoek CGM (2016) Word2visualvec: image and video to sentence matching by visual feature prediction. CoRRGoogle Scholar
  7. 7.
    Eisenschtat A, Wolf L (2016) Linking image and text with 2-way nets. Eprint ArXivGoogle Scholar
  8. 8.
    Fang H, Gupta S, Iandola F, Srivastava RK, Deng L, Dollar P, Gao J, He X, Mitchell M, Platt JC (2014) From captions to visual concepts and back. Eprint Arxiv, 1473–1482Google Scholar
  9. 9.
    Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Comput Vis Pattern Recogn 580–587Google Scholar
  10. 10.
    Ghosh S, Das N, Goncalves T, Quaresma P (2016) Representing image captions as concept graphs using semantic information. In: International conference on advances in computing, communications and informatics, pp 162–167Google Scholar
  11. 11.
    Gong Y, Ke Q, Isard M, Svetlana L (2013) A multi-view embedding space for modeling internet images, tags, and their semantics. Int J Comput Vis 106 (2):210–233CrossRefGoogle Scholar
  12. 12.
    Hardoon D R, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664CrossRefGoogle Scholar
  13. 13.
    He X, Zhang H, Kan MY, Chua TS (2016) Fast matrix factorization for online recommendation with implicit feedback. In: International ACM SIGIR conference on research and development in information retrieval. ACM, pp 549–558Google Scholar
  14. 14.
    Huang Y, Wang W, Wang L (2016) Instance-aware image and sentence matching with selective multimodal LSTM. Eprint ArXivGoogle Scholar
  15. 15.
    Jia Y, Salzmann M, Darrell T (2011) Learning cross-modality similarity for multinomial data. In: 2011 IEEE International conference on computer vision (ICCV). IEEE, pp 2407–2414Google Scholar
  16. 16.
    Karpathy A, Joulin A, Li FF (2014) Deep fragment embeddings for bidirectional image sentence mapping. Adv Neural Inf Process Syst 3:1889–1897Google Scholar
  17. 17.
    Kulis B, Grauman K (2009) Kernelized locality-sensitive hashing for scalable image search. In: IEEE 12th International conference on computer vision. IEEE, pp 2130–2137Google Scholar
  18. 18.
    Kumar S, Udupa R (2011) Learning hash functions for cross-view similarity search. Proc Int Joint Conf Artif Intell 22(1):1360–1365Google Scholar
  19. 19.
    Li J, Li J (2015) Fast image search with locality-sensitive hashing and homogeneous kernels map. The Scientific World JournalGoogle Scholar
  20. 20.
    Lv Q, Josephson W, Wang Z et al (2007) Multi-probe LSH: efficient indexing for high. In: Proceedings of international conference of very large data bases, pp 950–961Google Scholar
  21. 21.
    Ma L, Lu Z, Shang L, Li H (2015) Multimodal convolutional neural networks for matching image and sentence. In: IEEE International conference on computer vision, pp 2623–2631Google Scholar
  22. 22.
    Masci J, Meier U, Dan C et al (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks. Springer-Verlag, pp 52–59Google Scholar
  23. 23.
    Paulevé L, Jégou H, Amsaleg L (2010) Locality sensitive hashing: a comparison of hash function types and querying mechanisms. Pattern Recogn Lett 31 (11):1348–1358CrossRefGoogle Scholar
  24. 24.
    Plummer BA, Wang L, Cervantes CM, Caicedo JC (2015) Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: IEEE International conference on computer vision, 2641–2649Google Scholar
  25. 25.
    Sharma A, Kumar A, Daume H, Jacobs DW (2012) Generalized multiview analysis: a discriminative latent space. In: IEEE Conference on computer vision and pattern recognition, pp 2160–2167Google Scholar
  26. 26.
    Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. Comput SciGoogle Scholar
  27. 27.
    Socher R, Karpathy A, Le QV, Manning CD, Ng AY (2013) Grounded compositional semantics for finding and describing images with sentences. Nlp.stanford.eduGoogle Scholar
  28. 28.
    Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. Comput Sci 3156–3164Google Scholar
  29. 29.
    Wang C, Yang H, Bartz C, Meinel C (2016) Image captioning with deep bidirectional LSTMs. In: ACM Conference on multimedia, pp 988–997Google Scholar
  30. 30.
    Wu F, Lu X, Zhang Z et al (2013) Cross-media semantic representation via bi-directional learning to rank. In: Proceedings of the 21st ACM international conference on Multimedia. ACM, pp 877–886Google Scholar
  31. 31.
    Zhang H, Shang X, Luan H, Wang M, Chua TS (2016) Learning from collective intelligence: feature learning using social images and tags. ACM Trans Multimed Comput Commun Appl (TOMM) 13(1):1CrossRefGoogle Scholar
  32. 32.
    Zhang H, Shen F, Liu W et al (2016) Discrete collaborative filtering. In: International ACM SIGIR conference on research and development in information retrieval. ACM, pp 325–334Google Scholar
  33. 33.
    Zhuang Y, Yu Z, Wang W et al (2014) Cross-media hashing with neural networks. In: Proceedings of the ACM international conference on multimedia. ACM, pp 901–904Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yuhua Jia
    • 1
  • Liang Bai
    • 1
  • Peng Wang
    • 2
    Email author
  • Jinlin Guo
    • 1
  • Yuxiang Xie
    • 1
  • Tianyuan Yu
    • 1
  1. 1.Science and Technology on Information Systems Engineering LaboratoryNational University of Defense TechnologyChangshaChina
  2. 2.National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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