Skip to main content
SpringerLink
Log in

Collaborative hashing adopted in locality-constrained linear coding for scene classification

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

Abstract

Scene classification methods based on effective feature extraction and coding have obtained promising results in recent years. But the K-nearest neighbor search strategy in Locality-constrained Linear Coding (LLC) increases the complexity of the algorithm due to the exhaustive search. To solve the problem, an improved approximate nearest neighbor search strategy is proposed to improve the computational efficiency of LLC. Considering the mapping relationship between the visual words and features, a collaborative hashing method is incorporated to transform the high dimensional features into binary code form, and the original Euclidean space is transformed into the Hamming space that consists of multi similar features. The similar visual words can be queried quickly. Then the nearest neighbors can be searched efficiently through Hamming distance ranking, which can improve the coding efficiency. The experimental results on standard datasets demonstrate the effectiveness of the proposed approach, and the average classification accuracy can be improved.

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

Access this article

Log in via an institution

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. Alimjan G, Sun T, Liang Y, Jumahun H, Guan Y (2018) A new technique for remote sensing image classification based on combinatorial algorithm of SVM and KNN. IJPRAI 32(7):1–23

    Google Scholar 

  2. Andoni A, Indyk P, Laarhoven T, Razenshteyn IP, Schmidt L (2015) Practical and optimal LSH for angular distance. In: Advances in neural information processing systems 28: annual conference on neural information processing systems, Montreal, Quebec, Canada, pp 1225–1233

  3. Chen J, He X, Song X, Zhang H, Nie L, Chua TS (2018) Venue prediction for social images by exploiting rich temporal patterns in lbsns. In: MultiMedia Modeling, Bangkok,Thailand, pp 327–339

  4. Jin Z, Hu Y, Lin Y, Zhang D, Lin S, Cai D, Li X (2014) Complementary projection hashing. In: IEEE international conference on computer vision, pp 257–264

  5. Kong W, Li W (2012) Double-bit quantization for hashing. In: Proceedings of the twenty-sixth AAAI conference on artificial intelligence, Toronto, Ontario, Canada. AAAI Press, pp 634–640

  6. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE computer society conference on computer vision and pattern recognition, New York, NY, USA. IEEE, pp 2169–2178

  7. Liu X, He J, Lang B, Chang S (2013) Hash bit selection: a unified solution for selection problems in hashing. In: IEEE conference on computer vision and pattern recognition, Portland, OR, USA, pp 1570–1577

  8. Liu X, He J, Deng C, Lang B (2014) Collaborative hashing. In: IEEE conference on computer vision and pattern recognition, Columbus, OH, USA. IEEE Computer Society, pp 2147–2154

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

    Article  Google Scholar 

  10. Min H, Liang M, Luo R, Zhu J (2016) Laplacian regularized locality-constrained coding for image classification. Neurocomputing 171:1486–1495

    Article  Google Scholar 

  11. Moran S, Lavrenko V, Osborne M (2013) Variable bit quantisation for LSH. In: Proceedings of the 51st annual meeting of the association for computational linguistics, ACL, Sofia, Bulgaria, vol 2. The Association for Computer Linguistics, pp 753–758

  12. Nie L, Wang M, Gao Y, Zha ZJ, Chua TS (2013) Beyond text qa: multimedia answer generation by harvesting web information. IEEE Trans Multimedia 15(2):426–441

    Article  Google Scholar 

  13. Nie L, Zhao YL, Akbari M, Shen J (2015) Bridging the vocabulary gap between health seekers and healthcare knowledge. IEEE Trans Knowl Data Eng 27 (2):396–409

    Article  Google Scholar 

  14. Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312

    Article  Google Scholar 

  15. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: 9th IEEE international conference on computer vision, Nice, France. IEEE, pp 1470–1477

  16. Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: The twenty-third IEEE conference on computer vision and pattern recognition, San Francisco, CA, USA. IEEE, pp 3360–3367

  17. Xie L, Shen J, Zhu L (2016) Online cross-modal hashing for web image retrieval. In: AAAI, pp 294–300

  18. Xie L, Shen J, Han J, Zhu L, Shao L (2017) Dynamic multi-view hashing for online image retrieval. In: IJCAI, pp 3133–3139

  19. Yang J, Yu K, Gong Y, Huang TS (2009) Linear spatial pyramid matching using sparse coding for image classification. In: IEEE computer society conference on computer vision and pattern recognition, Miami, Florida, USA. IEEE, pp 1794–1801

  20. Yu K, Zhang T, Gong Y (2009) Nonlinear learning using local coordinate coding. In: 23rd annual conference on neural information processing systems, Vancouver, British Columbia, Canada. Curran Associates, Inc, pp 2223–2231

  21. Yu J, Tao D, Rui Y, Cheng J (2013) Pairwise constraints based multiview features fusion for scene classification. Pattern Recogn 46(2):483–496

    Article  MATH  Google Scholar 

  22. Zhang H, Berg AC, Maire M, Malik J (2006) SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: IEEE computer society conference on computer vision and pattern recognition, New York, NY, USA. IEEE Computer Society, pp 2126–2136

  23. Zhang D, Wang J, Cai D, Lu J (2010) Self-taught hashing for fast similarity search. In: SIGIR. ACM, New York, NY, US, pp 18-25

  24. Zhang Z, Wang Q, Ruan L, Si L (2014) Preference preserving hashing for efficient recommendation. In: The 37th international ACM SIGIR conference on research and development in information retrieval, Gold Coast, QLD, Australia. ACM, pp 183–192

  25. Zhou K, Zha H (2012) Learning binary codes for collaborative filtering. In: The 18th ACM SIGKDD international conference on knowledge discovery and data mining, Beijing, China. ACM, pp 498–506

Download references

Author information

Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Zhiping Zhou, Chunye Li & Fangzheng Zhou

  2. Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Zhiping Zhou

  3. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, People’s Republic of China

    Xiaoxiao Zhao

Authors
  1. Zhiping Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunye Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoxiao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fangzheng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiping Zhou.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Li, C., Zhao, X. et al. Collaborative hashing adopted in locality-constrained linear coding for scene classification. Multimed Tools Appl 78, 16329–16343 (2019). https://doi.org/10.1007/s11042-018-6978-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6978-1

Keywords

Search

Navigation