Skip to main content
SpringerLink
Log in

Applying Segmented Images by Louvain Method into Content-Based Image Retrieval

  • Conference paper
  • First Online:
Context-Aware Systems and Applications (ICCASA 2021)

Included in the following conference series:

  • 391 Accesses

Abstract

The amount of multimedia data has increased on personal computers and the Internet requires the essential to finding a particular image or a collection of images have enhanced of demands. It urges researchers to propose new sophisticated methods to retrieve the information one desires. In the case of, the legacy approach cannot grow up with the rapid rate of available data anymore. Therefore, content-based image retrieval (CBIR) has attracted many researchers to various fields. Content-based image retrieval models attempt to effort to automate data analysis and indexing. In this paper, we propose a content-based image retrieval system for real images. This method is using segmented images by the Louvain method [26] to create features in order to apply to the CBIR system based on the Bag-of-Visual-Words (BoVW) model. In order to evaluate the proposed method, we selected the Corel dataset which is composed of 10 classes [14] total of 1000 images in the dataset for the experiment. The experimental results are shown using qualitative and quantitative evaluations.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. 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). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  2. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theor. Exp. 10, 10008 (2008). https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  MATH  Google Scholar 

  3. Chaudhari, R., Patil, A.M.: Content based image retrieval using color and shape features. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 1, 386–392 (2012)

    Google Scholar 

  4. Choudhary, R., Raina, N., Chaudhary, N., Chauhan, R., Goudar, R.H.: An integrated approach to content based image retrieval. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2404–2410 (2014). https://doi.org/10.1109/ICACCI.2014.6968394

  5. Cortés, X., Conte, D., Cardot, H.: Bags of graphs for human action recognition. In: Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B., Robles-Kelly, A. (eds.) S+SSPR 2018. LNCS, vol. 11004, pp. 429–438. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97785-0_41

    Chapter  Google Scholar 

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

    Google Scholar 

  7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005. https://doi.org/10.1109/CVPR.2005.177

  8. Dang, Q.B., Coustaty, M., Luqman, M.M., Ogier, J.M.: A comparison of local features for camera-based document image retrieval and spotting. IJDAR 22(3), 247–263 (2019)

    Article  Google Scholar 

  9. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  10. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A K-Means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)

    Article  Google Scholar 

  11. Huang, Y.F., Chen, B.R.: Content-based image retrieval system for real images. In: Benzmüller, C., Sutcliffe, G., Rojas, R. (eds.) GCAI 2016. 2nd Global Conference on Artificial Intelligence. EPiC Series in Computing, vol. 41, pp. 95–108. EasyChair (2016). https://doi.org/10.29007/w4sr, https://easychair.org/publications/paper/Z3T

  12. Janani, R.G.: An improved CBIR method using color and texture properties with relevance feedback. Int. J. Innovative Res. Comput. Commun. Eng. 2, 47–54 (2014)

    Google Scholar 

  13. Khosla, G., Rajpal, N., Singh, J.: Evaluation of Euclidean and Manhanttan metrics in content based image retrieval system. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 12–18, March 2015

    Google Scholar 

  14. Liu, G.H., Yang, J.Y., Li, Z.: Content-based image retrieval using computational visual attention model. Pattern Recogn. 48(8), 2554–2566 (2015)

    Article  Google Scholar 

  15. Liu, J.: Image retrieval based on bag-of-words model. CoRR abs/1304.5168 (2013). http://arxiv.org/abs/1304.5168

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

    Article  Google Scholar 

  17. Mandal, R., Roy, P.P., Pal, U., Blumenstein, M.: Bag-of-visual-words for signature-based multi-script document retrieval. Neural Comput. Appl. 31(10), 6223–6247 (2018). https://doi.org/10.1007/s00521-018-3444-y

    Article  Google Scholar 

  18. Manzo, M., Pellino, S.: Bag of ARSRG words (BoAw). Mach. Learn. Knowl. Extr. 1, 871–882 (2019). https://doi.org/10.3390/make1030050

    Article  Google Scholar 

  19. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005). https://doi.org/10.1109/TPAMI.2005.188

    Article  Google Scholar 

  20. Mikolajczyk, K., et al.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1), 43–72 (2005). https://doi.org/10.1007/s11263-005-3848-x

    Article  Google Scholar 

  21. Mukherjee, J., Mukhopadhyay, J., Mitra, P.: A survey on image retrieval performance of different bag of visual words indexing techniques. In: Proceedings of the 2014 IEEE Students’ Technology Symposium, pp. 99–104 (2014). https://doi.org/10.1109/TechSym.2014.6807922

  22. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  23. Nguyen, T., Coustaty, M., Guillaume, J.: A new image segmentation approach based on the Louvain algorithm. In: 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–6 (2018). https://doi.org/10.1109/CBMI.2018.8516531

  24. Nguyen, T.: Image segmentation and extraction based on pixel communities. Segmentation et extraction d’images basées sur des communautés de pixels. Ph.D. Thesis, University of La Rochelle, France (2019). https://tel.archives-ouvertes.fr/tel-03223157

  25. Nguyen, T., Coustaty, M., Guillaume, J.: A combination of histogram of oriented gradients and color features to cooperate with Louvain method based image segmentation. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pp. 280–291. INSTICC, SciTePress (2019). https://doi.org/10.5220/0007389302800291

  26. Nguyen, T.K., Guillaume, J.L., Coustaty, M.: An enhanced Louvain based image segmentation approach using color properties and histogram of oriented gradients. In: Cláudio, A.P., et al. (eds.) Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 543–565. Springer International Publishing, Cham (2020)

    Chapter  Google Scholar 

  27. Okawa, M.: Offline signature verification based on bag-of-visualwords model using KAZE features and weighting schemes. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 252–258 (2016). https://doi.org/10.1109/CVPRW.2016.38

  28. Potapov, A., Zhdanov, I., Scherbakov, O., Skorobogatko, N., Latapie, H., Fenoglio, E.: Semantic image retrieval by uniting deep neural networks and cognitive architectures. CoRR abs/1806.06946 (2018). http://arxiv.org/abs/1806.06946

  29. Sadeghi-Tehran, P., Angelov, P., Virlet, N., Hawkesford, M.J.: Scalable database indexing and fast image retrieval based on deep learning and hierarchically nested structure applied to remote sensing and plant biology. J. Imaging 5(3), 33 (2019). https://doi.org/10.3390/jimaging5030033

    Article  Google Scholar 

  30. Silva, F., Goldenstein, S., Tabbone, S., Torres, R.: Image classification based on bag of visual graphs, pp. 4312–4316 (2013). https://doi.org/10.1109/ICIP.2013.6738888

  31. Silva, F.B., de O. Werneck, R., Goldenstein, S., Tabbone, S., da S. Torres, R.: Graph-based bag-of-words for classification. Pattern Recogn. 74, 266–285 (2018). https://doi.org/10.1016/j.patcog.2017.09.018

  32. Srivastava, P., Prakash, O., Khare, A.: Content-based image retrieval using moments of wavelet transform. In: The 2014 International Conference on Control, Automation and Information Sciences (ICCAIS 2014), pp. 159–164 (2014). https://doi.org/10.1109/ICCAIS.2014.7020550

  33. Tsai, C.F.: Bag-of-words representation in image annotation: a review. ISRN Artif. Intell. 2012 (2012). https://doi.org/10.5402/2012/376804

  34. Wan, J., et al.: Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22Nd ACM International Conference on Multimedia, pp. 157–166. MM 2014, ACM (2014). https://doi.org/10.1145/2647868.2654948

  35. Wang, H., Mohamad, D., Ismail, N.A.: Image retrieval: techniques, challenge, and trend. World Acad. Sci. Eng. Technol. 60, 716–718 (2009)

    Google Scholar 

  36. Wang, H.H., Mohamad, D., Ismail, N.A.: Approaches, challenges and future direction of image retrieval. CoRR abs/1006.4568 (2010). http://arxiv.org/abs/1006.4568

Download references

Author information

Authors and Affiliations

  1. Ca Mau Community College, Ca Mau, Vietnam

    Tuyet-Ngan Vo & Thanh-Khoa Nguyen

  2. L3i Laboratory, University of La Rochelle, La Rochelle, France

    Mickael Coustaty & Jean-Loup Guillaume

  3. Can Tho University, Can Tho, Vietnam

    De Cao Tran

Authors
  1. Tuyet-Ngan Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mickael Coustaty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Loup Guillaume
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thanh-Khoa Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. De Cao Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thanh-Khoa Nguyen .

Editor information

Editors and Affiliations

  1. Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

    Phan Cong Vinh

  2. University of the West of England, Bristol, UK

    Abdur Rakib

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vo, TN., Coustaty, M., Guillaume, JL., Nguyen, TK., Tran, D.C. (2021). Applying Segmented Images by Louvain Method into Content-Based Image Retrieval. In: Cong Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications. ICCASA 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 409. Springer, Cham. https://doi.org/10.1007/978-3-030-93179-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93179-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93178-0

  • Online ISBN: 978-3-030-93179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation