Skip to main content
SpringerLink
Log in

Content-Based Image Retrieval Using Fused Convolutional Neural Networks

  • Conference paper
  • First Online:
Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 (AISI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 152))

Abstract

Multimedia plays a significant role in our day-to-day life. Large image databases easily exist due to the spread of social websites, cloud storage, and smartphones. Searching by text is normal and easy, but searching by image’s content is more intelligent and sensitive. Therefore, this study develops a content-based image retrieval algorithm that achieved high image retrieval accuracy. Intelligent systems can help in this way and work effectively. This study investigates the three proposed models using deep learning algorithms. These models were applied on three databases of different sizes: Corel1K, Corel8K, Aloi74K, and Cifar-100. The first method uses a convolution neural network (CNN) for feature extraction and image classification. The second method uses CNN and long short-term memory (LSTM), and the last one employs CNN and gated recurrent unit (GRU). The three proposed algorithms have significantly reduced the time complexity. Additionally, they have achieved high image retrieval accuracy compared to state-of-the-art models. The proposed CNN model achieved 96%, 89%, and 98%, for Corel1K, Corel8K, and Aloi74K respectively. The CNN + LSTM model achieved CBIR accuracies of 96.5%, 90.5%, and 98.5% for Corel1k, Corel8k, and Aloi74k respectively. Finally, the CNN + GRU model achieved CBIR accuracies 97%, 91.5%, and 99% for Corel1k, Corel8k, and Aloi74k respectively.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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. Ghaleb, M.S., Ebied, H.M., Shedeed, H.A., Tolba, M.F.: COVID-19 X-rays model detection using convolution neural network. In: Hassanien, A.E., et al. (eds.) AICV 2021. AISC, vol. 1377, pp. 3–11. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-76346-6_1

    Chapter  Google Scholar 

  2. Rodríguez, A., Ariza, M., Gaona-García, P., Montenegro-Marin, C., Sarría, Í.: Image classification methods applied in immersive environments for fine motor skills training in early education. Int. J. Interact. Multimed. Artif. Intell. (2019)

    Google Scholar 

  3. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer (2016). arXiv:1606.05718

    Google Scholar 

  4. Zafar, B., Ashraf, R., Ali, N., Iqbal, M.K., Sajid, M., Dar, S.H., Ratyal, N.I.: A novel discriminating and relative global spatial image representation with applications in CBIR. Appl. Sci. (2018)

    Google Scholar 

  5. Shafaey, M.A., Salem, M.-M., Ebied, H.M., Al-Berry, M.N., Tolba, M.F.: Deep learning for satellite image classification. In: Hassanien, A.E., Tolba, M.F., Shaalan, K., Azar, A.T. (eds.) AISI 2018. AISC, vol. 845, pp. 383–391. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99010-1_35

    Chapter  Google Scholar 

  6. Ali, N., et al.: A novel image retrieval based on visual words integration of SIFT and SURF. PLoS ONE 11, e0157428 (2018)

    Article  Google Scholar 

  7. Haque M.R., Islam M.M., Iqbal H., Reza M.S., Hasan M.K.: Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. In: International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshah, pp. 1–5 (2018)

    Google Scholar 

  8. Ayon, S.I., Islam, M.M., Hossain, M.R.: Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J. Res. 1–20 (2020)

    Google Scholar 

  9. Mohanaiah, P., Sathyanarayana, P., GuruKumar, L.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3(5), 1–5 (2013)

    Google Scholar 

  10. Ghaleb, M.S., Ebied, H.M., Shedeed, H.A., Tolba, M.F.: Image retrieval based on self-organizing feature map and multilayer perceptron neural networks classifier.In: Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, pp. 189–193 (2019)

    Google Scholar 

  11. Liapis, S., Tziritas, G.: Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans. Multimed. 676–686 (2004)

    Google Scholar 

  12. Anami, B.S., Suvarna, S.N., Govardhan, A.: A combined color, texture and edge features based approach for identification and classification of Indian medicinal plants. Int. J. Comput. Appl. 45–51 (2010)

    Google Scholar 

  13. Che, H.N.A., Jamil, N., Nordin, S., Awang, K.: Plant species identification by using Scale Invariant Feature Transform (SIFT) and Grid Based Colour Moment (GBCM). In: IEEE Conference on Open Systems (ICOS), Kuching, pp. 226–230 (2013)

    Google Scholar 

  14. Vanishree, Ramana, R.K.V: Implementation of pipelined Sobel edge detection algorithm on FPGA for High speed applications. In:International Conference on Emerging Trends in Communication, Control, Signal Processing Computing Applications (C2SPCA), pp. 1–5 (2013)

    Google Scholar 

  15. Sezavar, A., Farsi, H., Mohamadzadeh, S.: Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimed. Tools Appl. 78, 20895–20912 (2019)

    Google Scholar 

  16. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, pp. 153–160 (2007)

    Google Scholar 

  17. Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using googlenet and directional feature maps. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 846–850. IEEE (Aug 2015)

    Google Scholar 

  18. Fukushima, K.: Neocognitron. Scholarpedia 2(1), 1717 (2007)

    Article  MathSciNet  Google Scholar 

  19. Talo, M., Yildirim, O., Baloglu, U.B., Aydin, G., Acharya, U.R: Convolutional neural networks for multiclass brain disease detection using MRI images, vol. 78 (2019)

    Google Scholar 

  20. Celik, Y., Talo, M., Yildirim, O., Karabatak, M., Acharya, U.R.: Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recogn Lett 133, 232–239 (2020)

    Google Scholar 

  21. Li, L., Feng, L., Wu, J., Sun, M.X., Liu, S.l.: Exploiting global and local features for image retrieval. J. Cent. South Univ. 259–276 (2018)

    Google Scholar 

  22. Farsi, H., Mohamadzadeh, S.: Colour and texture feature-based image retrieval by using Hadamard matrix in discrete wavelet transform. IET Image Process, 212–218 (2013)

    Google Scholar 

  23. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 436–444 (2015)

    Google Scholar 

  24. Jiang, X.: Feature extraction for image recognition and computer vision. In: 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, vol. 2009, pp. 1–15 (2009)

    Google Scholar 

  25. Saritha, R.R., Paul, V., Kumar, P.G.: Content based image retrieval using deep learning process. Clust. Comput. 22(2), 4187–4200 (2018). https://doi.org/10.1007/s10586-018-1731-0

    Article  Google Scholar 

  26. Zhao, G., Liu, G., Fang, L., Tu, B., Ghamisi, P.: Multiple convolutional layers fusion framework for hyperspectral image classification. Neurocomputing 339, 149–160 (2019)

    Google Scholar 

  27. Tan, J.W., Chang, S.-W., Abdul-Kareem, S., Yap, H.J., Yong, K.-T.: Deep learning for plant species classification using leaf vein morphometric. In: IEEE/ACM Trans. Comput. Biology Bioinf. 17(1), 82–90 (1 Jan.–Feb. 2020)

    Google Scholar 

  28. Nagda, M.E., Poovammal, E.: Image classification using a hybrid LSTM-CNN deep neural network (2019)

    Google Scholar 

  29. Islam, M.Z., Islam, M.M., Asraf, A.: A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inf. Med. Unlocked 20 (2020)

    Google Scholar 

  30. Ahmed, A.: Pre-trained CNNs models for content based image retrieval. Int. J. Adv. Comput. Sci. Appl. (2021)

    Google Scholar 

  31. Nguyen, V., Cai, J., Chu, J.: Hybrid CNN-GRU model for high efficient handwritten digit recognition. In: Association for Computing Machinery, pp. 66–71 (2019)

    Google Scholar 

  32. Yu, F.-X., Luo, H., Lu, Z.-M.: Colour image retrieval using pattern co-occurrence matrices based on BTC and VQ. Electron. Lett. 47(2), 100–101 (2011)

    Article  Google Scholar 

  33. Guo, J.-M., Prasetyo, H.: Content-based image retrieval using features extracted from halftoning-based block truncation coding. IEEE Trans. Image Process. 23, 1010–1024 (2015)

    MathSciNet  MATH  Google Scholar 

  34. Guo, J.-M., Prasetyo, H., Wang, N.-J.: Effective image retrieval system using dot-diffused block truncation coding features. IEEE Trans. Multimed. 17,1576–1590 (2015)

    Google Scholar 

  35. Charles, Y.R., Ramraj, R.: A novel local mesh color texture pattern for image retrieval system. Int. J. Electron. Commun. 70, 225–233 (2016)

    Google Scholar 

  36. Mohamadzadeh, S., Farsi, H.: Content-based image retrieval system via sparse representation. IET Comput. Vision 95–102 (2014)

    Google Scholar 

  37. Pardijs, M., User-centered reduction of the semantic gap in content-based image retrieval

    Google Scholar 

  38. Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory, vol. 9, pp. 1735–1780. Cambridge, MA, USA (1997)

    Google Scholar 

  39. Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y. (2014)

    Google Scholar 

  40. LeCun, Y., Haffner, P., Bottou, L., Bengio, Y.: Object recognition with gradient-based learning. In: Shape, Contour and

    Google Scholar 

  41. Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam library of object images. Int. J. Comput. Vision 61, 103–112 (2005)

    Article  Google Scholar 

  42. Coral dataset, last referred on June 2009. Available at http://wang.ist.psu.edu/docs/related/

  43. Walia, E., Pal, A.: Fusion framework for effective color image retrieval. J. Vis. Commun. Image Represent. 25 (2014)

    Google Scholar 

  44. ElAlami, M.E.: A new matching strategy for content based image retrieval system. Appl. Soft Comput. (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Scientific Computing Department, Ain Shams University, Cairo, Egypt

    Moshira S. Ghaleb, Hala M. Ebied, Howida A. Shedeed & Mohamed F. Tolba

Authors
  1. Moshira S. Ghaleb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hala M. Ebied
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Howida A. Shedeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed F. Tolba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moshira S. Ghaleb .

Editor information

Editors and Affiliations

  1. Faculty of Computers Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Mincong Tang

  4. College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China

    Tien-Wen Sung

  5. Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghaleb, M.S., Ebied, H.M., Shedeed, H.A., Tolba, M.F. (2023). Content-Based Image Retrieval Using Fused Convolutional Neural Networks. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_24

Download citation

Publish with us

Policies and ethics

Search

Navigation