Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computer Vision and Image Processing

CVIP 2019: Computer Vision and Image Processing pp 407–418Cite as

Two Efficient Image Bag Generators for Multi-instance Multi-label Learning

  • Conference paper
  • First Online:

  • 719 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1147))

Abstract

Image annotation plays a vital role in dealing with effective organization and retrieval of a large number of digital images. Multi-instance multi-label (MIML) learning can deal with complicated objects by solving the ambiguity in both input and output space. Image bag generator is a key component of MIML algorithms. A bag generator takes an image as its input and generates a set of instances for that image. These instances are the various subparts of the original image and collectively describe the image in totality. This paper proposes two new bag generators which can generate an instance for every possible object present in the image. The proposed bag generators effectively utilize the correlations among pixels to generate instances. We demonstrate that the proposed bag generators outperform the state-of-the-art bag generator methods.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bhagat, P.K., Choudhary, P.: Image annotation: then and now. Image Vis. Comput. 80, 1–23 (2018). https://doi.org/10.1016/j.imavis.2018.09.017. http://www.sciencedirect.com/science/article/pii/S0262885618301628

    Article  Google Scholar 

  2. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004). https://doi.org/10.1016/j.patcog.2004.03.009

    Article  Google Scholar 

  3. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002). https://doi.org/10.1109/TPAMI.2002.1023800

    Article  Google Scholar 

  4. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.T.: NUS-WIDE: a real-world web image database from national university of Singapore. In: Proceedings of ACM Conference on Image and Video Retrieval (CIVR 2009), Santorini, Greece, 8–10 July 2009 (2009)

    Google Scholar 

  5. Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800–810 (2001). https://doi.org/10.1109/34.946985

    Article  Google Scholar 

  6. Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997). https://doi.org/10.1016/S0004-3702(96)00034-3. http://dx.doi.org/10.1016/S0004-3702(96)00034-3

    Article  MATH  Google Scholar 

  7. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC 3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  8. He, J., Gu, H., Wang, Z.: Bayesian multi-instance multi-label learning using Gaussian process prior. Mach. Learn. 88(1), 273–295 (2012). https://doi.org/10.1007/s10994-012-5283-x

    Article  MathSciNet  MATH  Google Scholar 

  9. Huang, S., Zhou, Z.: Fast multi-instance multi-label learning. CoRR abs/1310.2049 (2013)

    Google Scholar 

  10. Lerski, R., Straughan, K., Schad, L., Boyce, D., Bluml, S., Zuna, I.: VIII. MR image texture analysis-an approach to tissue characterization. J. Magn. Reson. Imaging 11(6), 873–887 (1993). https://doi.org/10.1016/0730-725X(93)90205-R

    Article  Google Scholar 

  11. Liu, W., Xu, W., Li, L., Li, G.: Two new bag generators with multi-instance learning for image retrieval. In: 3rd IEEE Conference on Industrial Electronics and Applications, June 2008, pp. 255–259 (2008). https://doi.org/10.1109/ICIEA.2008.4582518

  12. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  13. Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems 10, NIPS 1997, pp. 570–576. MIT Press, Cambridge (1998). http://dl.acm.org/citation.cfm?id=302528.302753

  14. Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 341–349. Morgan Kaufmann Publishers Inc., San Francisco (1998)

    Google Scholar 

  15. Nasierding, G., Tsoumakas, G., Kouzani, A.Z.: Clustering based multi-label classification for image annotation and retrieval. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 4514–4519, October 2009

    Google Scholar 

  16. Nguyen, C.T., Zhan, D.C., Zhou, Z.H.: Multi-modal image annotation with multi-instance multi-label LDA. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 1558–1564. AAAI Press (2013)

    Google Scholar 

  17. Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_38

    Chapter  Google Scholar 

  18. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  19. Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135–168 (2000). https://doi.org/10.1023/A:1007649029923

    Article  MATH  Google Scholar 

  20. Wang, J., Zucker, J.D.: Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the Seventeenth International Conference on Machine Learning, ICML 2000, pp. 1119–1126. Morgan Kaufmann Publishers Inc., San Francisco (2000)

    Google Scholar 

  21. Wei, X.-S., Zhou, Z.-H.: An empirical study on image bag generators for multi-instance learning. Mach. Learn. 105(2), 155–198 (2016). https://doi.org/10.1007/s10994-016-5560-1

    Article  MathSciNet  MATH  Google Scholar 

  22. Wu, B., Lyu, S., Hu, B.G., Ji, Q.: Multi-label learning with missing labels for image annotation and facial action unit recognition. Pattern Recognit. 48(7), 2279–2289 (2015). https://doi.org/10.1016/j.patcog.2015.01.022

    Article  Google Scholar 

  23. Xu, X., Frank, E.: Logistic regression and boosting for labeled bags of instances. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 272–281. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24775-3_35

    Chapter  Google Scholar 

  24. Zhang, C., Chen, S.C., Shyu, M.L.: Multiple object retrieval for image databases using multiple instance learning and relevance feedback. In: IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No. 04TH8763), vol. 2, pp. 775–778, June 2004. https://doi.org/10.1109/ICME.2004.1394315

  25. Zhang, Q., Goldman, S.A., Yu, W., Fritts, J.: Content-based image retrieval using multiple-instance learning. In: Proceedings of the Nineteenth International Conference on Machine Learning, ICML 2002, pp. 682–689. Morgan Kaufmann Publishers Inc., San Francisco (2002). http://dl.acm.org/citation.cfm?id=645531.656002

  26. Zhou, Z.H, Zhang, M.L.: Multi-instance multi-label learning with application to scene classification. In: Schölkopf, B., Platt, J.C., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19, pp. 1609–1616. MIT Press (2007)

    Google Scholar 

  27. Zhou, Z.H., Zhang, M.L., Chen, K.J.: A novel bag generator for image database retrieval with multi-instance learning techniques. In: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 565–569, November 2003. https://doi.org/10.1109/TAI.2003.1250242

  28. Zhou, Z.H., Zhang, M.L., Huang, S.J., Li, Y.F.: Multi-instance multi-label learning. Artif. Intell. 176(1), 2291–2320 (2012). https://doi.org/10.1016/j.artint.2011.10.002

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Manipur, Imphal, India

    P. K. Bhagat & Kh Manglem Singh

  2. Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, HP, India

    Prakash Choudhary

Authors
  1. P. K. Bhagat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prakash Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kh Manglem Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. K. Bhagat .

Editor information

Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bhagat, P.K., Choudhary, P., Singh, K.M. (2020). Two Efficient Image Bag Generators for Multi-instance Multi-label Learning. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4015-8_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4014-1

  • Online ISBN: 978-981-15-4015-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation