Skip to main content
SpringerLink
Log in

Bag of Features vs Vector of Locally Aggregated Descriptors

  • Conference paper
  • First Online:
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 16))

Included in the following conference series:

Abstract

Image representation by set of local features are common and also state-of-the art for many applications such as image retrieval and image classification. A single image contains on average 2.5 k–3.0 k features. Searching the images based on local features are discriminative compared to global features at the cost of heavy computational overhead. Bag-of-Features (BoF), also known as bag-of-visual words, are used for feature quantization which makes searching local features feasible in very large databases at the cost of distinctiveness. Mostly, the vocabulary size in those applications is kept up-to 1 million. In this research study, we investigated the performance of Vector of Locally Aggregated Descriptors (VLAD) which is recently proposed as an alternative to BoF for different families of descriptor. The VLAD achieves similar or sometimes better performance when compared to BoF despite of limited vocabulary size. The performance of VLAD is mostly compared with BoF on gradient based descriptors in literature. In our experiments, we take gradient based descriptor, intensity based descriptor, and binary descriptor. Scale Invariant Feature Transform (SIFT), Local Intensity Order Pattern (LIOP) and BInarization of Gradient Orientation Histograms (BIGOH) are used to validate the performance of VLAD in parallel to BoF on famous benchmark dataset. VLAD outperforms BoF in gradient based family and intensity based family but non of these are feasible for binary descriptors.

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

Access this chapter

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Institutional subscriptions

Notes

  1. 1.

    http://qixianbiao.github.io/Scene.html.

  2. 2.

    http://research.microsoft.com/en-us/projects/objectclassrecognition/.

  3. 3.

    http://www.vlfeat.org/.

  4. 4.

    http://vlfeat.org/.

  5. 5.

    http://www.robots.ox.ac.uk/vgg/research/affine/.

References

  1. Yu, F.X., Ji, R., Tsai, M.-H., Ye, G., Chang, S.-F.: Weak attributes for large-scale image retrieval. In: International Conference on Computer Vision and Pattern Recognition, pp. 2949–2956 (2012)

    Google Scholar 

  2. Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)

    Article  Google Scholar 

  3. Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  4. Baber, J., Dailey, M.N., Satoh, S., Afzulpurkar, N., Bakhtyar, M.: BIG-OH: binarization of gradient orientation histograms. Image Vis. Comput. 32(11), 940–953 (2014)

    Article  Google Scholar 

  5. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  6. Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: Computer Vision and Pattern Recognition, pp. 25–32 (2009)

    Google Scholar 

  7. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  8. Jégou, H., Douze, M., Schmid, C.: Packing Bag-of-Features. In: International Conference on Computer Vision, pp. 2357–2364 (2009)

    Google Scholar 

  9. Baber, J., Afzulpurkar, N., Satoh, S.: A framework for video segmentation using global and local features. Int. J. Pattern Recogn. Artif. Intell. 27(05) (2013)

    Google Scholar 

  10. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: International Conference on Computer Vision, pp. 1470–1477 (2003)

    Google Scholar 

  11. Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: International Conference on Computer Vision and Pattern Recognition, pp. 3304–3311 (2010)

    Google Scholar 

  12. Yuan, X., Yu, J., Qin, Z., Wan, T.: A SIFT-LBP image retrieval model based on bag of features. In: IEEE International Conference on Image Processing (2011)

    Google Scholar 

  13. Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 603–610. IEEE (2011)

    Google Scholar 

  14. Yu, S., Jurie, F.: Improving image classification using semantic attributes. Int. J. Comput. Vis. 100(1), 59–77 (2012)

    Article  Google Scholar 

  15. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  16. Xiao, J., Hays, J., Ehinger, K., Oliva, A., Torralba, A., et al.: Sun database: large-scale scene recognition from abbey to zoo. In: International Conference on Computer Vision and Pattern Recognition, pp. 3485–3492 (2010)

    Google Scholar 

  17. Baber, J., Satoh, S., Afzulpurkar, N., Keatmanee, C.: Bag of visual words model for videos segmentation into scenes. In: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service, pp. 191–194 (2013)

    Google Scholar 

  18. Hota, A.: Comparison of some bag-of-words models for image recognition. In: 2014 X International Symposium on Telecommunications (BIHTEL), pp. 1–5 (2014)

    Google Scholar 

  19. Peng, X., Wang, L., Qiao, Y., Peng, Q.: Boosting VLAD with supervised dictionary learning and high-order statistics. In: European Conference on Computer, pp. 660–674 (2014)

    Google Scholar 

  20. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Computer Vision and Pattern Recognition, vol. 2, pp. 2161–2168 (2006)

    Google Scholar 

  21. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: European Conference on Computer, pp. 143–156 (2010)

    Google Scholar 

  22. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  23. Adam, B.: Reliable feature matching across widely separated views. In: International Conference on Computer Vision and Pattern Recognition, pp. 774–781 (2000)

    Google Scholar 

  24. Lindeberg, T., Gårding, J.: Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure. Image Vis. Comput. 15, 415–434 (1997)

    Article  Google Scholar 

  25. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60, 63–86 (2004)

    Article  Google Scholar 

  26. Malisiewicz, T., Gupta, A., Efros, A., et al.: Ensemble of exemplar-SVMs for object detection and beyond. In: International Conference on Computer Vision, pp. 89–96 (2011)

    Google Scholar 

Download references

Acknowledgment

This research work is supported by Higher Education Commission (HEC) of Pakistan, SBK women university, and university of Balochistan.

Author information

Authors and Affiliations

  1. Department of Computer Science, Sardar Bahadur Khan Women’s University, Quetta, Pakistan

    Farkhunda Younas

  2. Department of Computer Science and Information Technology, University of Balochistan, Quetta, Pakistan

    Junaid Baber & Maheen Bakhtyar

  3. Department of Computer Science, COMSATS Institute of Infomation Technology, Islamabad, Pakistan

    Tahir Mahmood

  4. Department of Electronic Engineering, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan

    Javeria Farooq

Authors
  1. Farkhunda Younas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junaid Baber
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tahir Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javeria Farooq
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maheen Bakhtyar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junaid Baber .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Younas, F., Baber, J., Mahmood, T., Farooq, J., Bakhtyar, M. (2018). Bag of Features vs Vector of Locally Aggregated Descriptors. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-56991-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56991-8_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56990-1

  • Online ISBN: 978-3-319-56991-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation