Skip to main content
Log in

Accumulated reconstruction error vector (AREV): a semantic representation for cross-media retrieval

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

Abstract

Cross-media retrieval aims to automatically perform the content-based search procedure among various media types (e.g., image, video and text), in which media representation plays an important role for providing the heterogeneous similarity measure. In this work, a novel semantic representation of cross-media, called accumulated reconstruction error vector (AREV), is proposed, which includes category-specific dictionary learning, media sample reconstruction, and accumulative reconstruction error concatenation. Instead of directly learning the correlation relationship among heterogeneous items in the same semantic groups, the AREV projects individually their original feature descriptions into a shared semantic space, in which each component is semantic consistent for various media types due to the consistency in category information. Experiments on the commonly used datasets, i.e. Wikipedia dataset and NUS-Wide dataset, show the good performance in terms of effectiveness and efficiency.

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

Access this article

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

Similar content being viewed by others

References

  1. Baidu Image Search, http://stu.baidu.com/

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

    MATH  Google Scholar 

  3. Broilo M, De Natale FG (2010) A stochastic approach to image retrieval using relevance feedback and particle swarm optimization. IEEE Trans Multimed 12(4):267–277

    Article  Google Scholar 

  4. Chandrasekhar V, Sharifi M, Ross DA (2011) Survey and evaluation of audio fingerprinting schemes for mobile query-by-example applications. In: International Society for Music Information Retrieval, pp. 801–806, ISMIR

  5. Chua TS, Tang JH, Hong RC, Li HJ, Luo ZP, Zheng YT (2009) NUS-WIDE: a real-world web image database from National University of Singapore. ACM Int Conf Image Video Retr. Greece. Jul. 8–10

  6. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer vision and pattern recognition, CVPR 2005. IEEE Computer Society Conference on vol. 1, pp. 886–893, IEEE

  7. Daras P, Manolopoulou S, Axenopoulos A (2012) Search and retrieval of rich media objects supporting multiple multimodal queries. IEEE Trans Multimed 14(3):734–746

    Article  Google Scholar 

  8. Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262, ACM

  9. Google Image Search, http://images.google.com/

  10. Guo G, Li SZ (2003) Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw 14(1):209–215

    Article  Google Scholar 

  11. Han YH, Yang Y, Ma ZG, Shen HQ, Sebe N, Zhou XF (2014) Image attribute adaptation. IEEE Trans Multimed. doi:10.1109/TMM.2014.2306092

    Google Scholar 

  12. Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: European Conference on Computer Vision. Springer, Berlin Heidelberg, pp. 304–317, ECCV

  13. Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 119–126, ACM

  14. Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Tran Multimed Comput Commun Appl (TOMCCAP) 2(1):1–19

    Article  Google Scholar 

  15. Ling L, Zhai X, Peng Y (2012) Tri-space and ranking based heterogeneous similarity measure for cross-media retrieval. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), pp. 230–233, IEEE

  16. Liu TY (2009) Learning to rank for information retrieval. Found Trends Inf Retr 3(3):225–331

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Lu G (2001) Indexing and retrieval of audio: a survey. Multimed Tools Appl 15(3):269–290

    Article  MATH  Google Scholar 

  19. 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, pp. 897–906, ACM

  20. Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet GR, Levy R, Vasconcelos N (2010) A new approach to cross-modal multimedia retrieval. In: Proceedings of the International Conference on Multimedia, pp. 251–260, ACM

  21. Salton G, Allan J, Buckley C (1993) Approaches to passage retrieval in full text information systems. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49–58, ACM

  22. Singhal A (2001) Modern information retrieval: a brief overview. IEEE Data Eng Bull 24(4):35–43

    Google Scholar 

  23. Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: Proceedings of Ninth IEEE International Conference on Computer Vision, pp. 1470–1477, IEEE

  24. Wang SH, Huang QM, Jiang SQ, Tian Q (2012) S3MKL: scalable semi-supervised multiple kernel learning for real world image data mining. IEEE Trans Multimed 14(4):1259–1274

    Article  Google Scholar 

  25. Wang SH, Huang QM, Jiang SQ, Tian Q (2012) Nearest-neighbor method using multiple neighborhood similarities for social media data mining. Neurocomputing 95(15):105–116

    Article  Google Scholar 

  26. Wang SH, Jiang SQ, Huang QM, Tian Q (2012) Multi-feature metric learning with knowledge transfer among semantics and social tagging. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)

  27. Wang Z, Liu G, Yang Y (2012) A new ROI based image retrieval system using an auxiliary Gaussian weighting scheme. Multimed Tools Appl 1–21

  28. Wei S, Xu D, Li X, Zhao Y (2013) Joint optimization toward effective and efficient image search. IEEE Trans Cybern, online published

  29. Wei S, Zhao Y, Zhu Z, Liu N (2010) Multimodal fusion for video search reranking. IEEE Trans Knowl Data Eng 22(8):1191–1199

    Article  Google Scholar 

  30. Wei S, Zhao Y, Zhu C, Xu C, Zhu Z (2011) Frame fusion for video copy detection. IEEE Trans Circ Syst Video Technol 21(1):15–28

    Article  Google Scholar 

  31. Xu Z, Yang Y, Tsang I, Sebe N, Hauptmann A (2013) Feature weighting via optimal thresholding for video analysis. Int Conf Comput Vis

  32. Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Analy Mach Intell 34(4):723–742

    Article  Google Scholar 

  33. Yang Y, Xu D, Nie F, Luo J, Zhuang Y (2009) Ranking with local regression and global alignment for cross media retrieval. In: Proceedings of the 17th ACM International Conference on Multimedia, pp. 175–184, ACM

  34. Yang Y, Zhuang YT, Wu F, Pan YH (2008) Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. IEEE Trans Multimed 10(3):437–446

    Article  Google Scholar 

  35. Younessian E, Rajan D (2013) Multi-modal fusion for associated news story retrieval. Multimed Tools Appl, pp. 1–23

Download references

Acknowledgments

This work was supported in part by the 973 Program (No. 2012CB316400), PCSIRT (No.IRT201206), the National Science Foundation of China (No.61202241, No.61210006, and No.61025013), the Fundamental Research Funds for the Central Universities (No.2013JBM024), and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shikui Wei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, K., Wei, S., Zhao, Y. et al. Accumulated reconstruction error vector (AREV): a semantic representation for cross-media retrieval. Multimed Tools Appl 74, 561–576 (2015). https://doi.org/10.1007/s11042-014-1968-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1968-4

Keywords

Navigation