Skip to main content
SpringerLink
Log in

The Bag-of-Words Methods with Pareto-Fronts for Similar Image Retrieval

  • Conference paper
  • First Online:
Information and Software Technologies (ICIST 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 756))

Included in the following conference series:

Abstract

This paper presents an algorithm for similar image retrieval which is based on the Bag-of-Words model. In Computer Vision the classic BoW algorithm is mainly used in image classification. Its operation is based on processing of one image, creating a visual words dictionary, and specifying the class to which a query image belongs. In the presented modification of the BoW algorithm two different image feature have been chosen, namely a visual words’ occurrence frequency histogram and a color histogram. As a result, using multi-criteria comparison, which so far has not been used in the BoW algorithms, a set of images similar to a query image is obtained, which is located on the Pareto-optimal non-dominated solutions front.

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 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

Institutional subscriptions

References

  1. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)

    Google Scholar 

  2. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  3. Gabryel, M., Grycuk, R., Korytkowski, M., Holotyak, T.: Image indexing and retrieval using GSOM algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, Lotfi A., Zurada, Jacek M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 706–714. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_63

    Chapter  Google Scholar 

  4. Gabryel, M., Capizzi, G.: The bag-of-words method with dictionary analysis by evolutionary algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, Lotfi A., Zurada, Jacek M. (eds.) ICAISC 2017. LNCS, vol. 10246, pp. 43–51. Springer, Cham (2017). doi:10.1007/978-3-319-59060-8_5

    Chapter  Google Scholar 

  5. Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: 2005 Tenth IEEE International Conference on Computer Vision, ICCV 2005, Vol. 1, pp. 370–377 (2005). doi:10.1109/ICCV.2005.77

  6. Chang, B.-M., Tsai, H.-H., Chou, W.-L.: Using visual features to design a content-based image retrieval method optimized by particle swarm optimization algorithm. Eng. Appl. Artif. Intell. 26(10), 2372–2382 (2013). doi:10.1016/j.engappai.2013.07.018

    Article  Google Scholar 

  7. Nanni, L., Melucci, M.: Combination of projectors, standard texture descriptors and bag of features for classifying images. Neurocomputing 173, 1062–1614 (2015). doi:10.1016/j.neucom.2015.09.032

    Google Scholar 

  8. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 2169–2178 (2006). doi:10.1109/CVPR.2006.68

  9. Li, W., Dong, P., Xiao, B., Zhou, L.: Object recognition based on the region of interest and optimal bag of words model. Neurocomputing 172, 271–280 (2016). doi:10.1016/j.neucom.2015.01.083

    Article  Google Scholar 

  10. Audet, S.: JavaCV. http://bytedeco.org/. Accessed 22 May 2017

  11. Bradski, G.: The OpenCV Library, Dr. Dobb’s Journal of Software Tools

    Google Scholar 

  12. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: 2004 Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2004, pp. 178–178 (2004). doi:10.1109/CVPR.2004.109

  13. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32

    Chapter  Google Scholar 

  14. Woźniak, M.: Novel image correction method based on swarm intelligence approach. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 404–413. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_32. ISBN 1865-0929

    Chapter  Google Scholar 

  15. Woźniak, M., Połap, D., Napoli, C., Tramontana, E.: Graphic object feature extraction system based on cuckoo search algorithm. Expert Syst. Appl. 66, 20–31 (2016). doi:10.1016/j.eswa.2016.08.068. Elsevier

    Article  Google Scholar 

  16. Gabryel, M.: The bag-of-features algorithm for practical applications using the MySQL database. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, Lotfi A., Zurada, Jacek M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 635–646. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_56

    Google Scholar 

  17. Gabryel, M.: A bag-of-features algorithm for applications using a NoSQL database. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 332–343. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_26

    Chapter  Google Scholar 

  18. Damaševičius, R., Maskeliūnas, R., Venčkauskas, A., Woźniak, M.: Smartphone user identity verification using gait characteristics. Symmetry 8(10), 1001–10020 (2016). doi:10.3390/sym8100100

    Google Scholar 

  19. Cpalka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Trans. Neural Netw. 20(4), 701–714 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Marcin Gabryel

Authors
  1. Marcin Gabryel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Gabryel .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Mikašytė

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Gabryel, M. (2017). The Bag-of-Words Methods with Pareto-Fronts for Similar Image Retrieval. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67642-5_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67641-8

  • Online ISBN: 978-3-319-67642-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation