Skip to main content
SpringerLink
Log in

Distributed Deep Learning for Content-Based Image Retrieval

  • Conference paper
  • First Online:
Machine Learning, Image Processing, Network Security and Data Sciences

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 946))

  • 873 Accesses

Abstract

In content-based image retrieval (CBIR), the main objective is to obtain the best possible feature of an image. Traditionally, color, texture and shape were used to extract the features of image. But as the deep learning era has started, researchers started using different deep learning models for extracting the features. The main problem with these deep learning models is the time that it takes for training the model. To overcome this difficulty, the distributed deep learning (DDL) can be used to reduce the total training time by training the model in a distributed environment. In this paper, we performed DDL using AlexNet architecture for CBIR. The three modes in DDL which are used in this paper are synchronous, asynchronous and Hogwild. The execution is done on these modes along with the deep learning version with no distribution. These modes are executed on the self-made in-house ‘Hadoop and Spark’ clusters which are 1 + 4 node cluster and 1 + 15 node cluster. The time and model accuracies of the different modes are compared. To further analyze the model, the five performance measures: average precision ratio (APR), F-score, average recall ratio (ARR), total minimum retrieval epoch (TMRE), and average normalized modified retrieval rank (ANMRR) are evaluated and compared.

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
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover 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. Singha M, Hemachandran K (2012) Content based image retrieval using color and texture. Signal Image Process 3(1):39

    Google Scholar 

  2. Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 762–768

    Google Scholar 

  3. Chun YD, Kim NC, Jang IH (2008) Content-based image retrieval using multiresolution color and texture features. IEEE Trans Multimedia 10(6):1073–1084

    Article  Google Scholar 

  4. Bhunia AK, Bhattacharyya A, Banerjee P, Roy PP, Murala S (2018) A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmet ric co-occurrence texture pattern, arXiv preprint arXiv:1801.00879

  5. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  6. Zhang B, Gao Y, Zhao S, Liu J (2009) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544

    Article  MathSciNet  MATH  Google Scholar 

  7. Verma M, Raman B, Murala S (2015) Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 165:255–269

    Article  Google Scholar 

  8. Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886

    Article  MathSciNet  MATH  Google Scholar 

  9. Heikkilä M, Pietikäinen M, Schmid C (2006) Description of interest regions with center-symmetric local binary patterns. In: Computer vision, graphics and image processing, Springer, Berlin, pp 58–69

    Google Scholar 

  10. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

    Article  Google Scholar 

  11. Clausi DA (2002) An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sens 28(1):45–62

    Article  Google Scholar 

  12. Hu RX, Jia W, Ling H, Zhao Y, Gui J (2013) Angular pattern and binary angular pattern for shape retrieval. IEEE Trans Image Process 23(3):1118–1127

    MathSciNet  MATH  Google Scholar 

  13. Hu R, Barnard M, Collomosse J (2010) Gradient field descriptor for sketch based retrieval and localization. In: 2010 IEEE international conference on image processing, pp 1025–1028

    Google Scholar 

  14. Mathew SP, Balas VE, Zachariah KP (2015) A content-based image retrieval system based on convex hull geometry. Acta Polytechnica Hungarica 12(1):103–116

    Google Scholar 

  15. Osowski S (2002) Fourier and wavelet descriptors for shape recognition using neural net- works—a comparative study. Pattern Recogn 35(9):1949–1957

    Article  MATH  Google Scholar 

  16. Rui Y, Huang TS, Chang SF (1999) Image retrieval: Current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39–62

    Article  Google Scholar 

  17. Smeulders AW, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  18. Kokare M, Chatterji BN, Biswas PK (2002) A survey on current content based image retrieval methods. IETE J Res 48(3–4):261–271

    Article  Google Scholar 

  19. Zheng L, Yang Y, Tian QSIFT, CNN SM (2018) A decade survey of instance retrieval. IEEE Trans Pattern Anal Mach Intell 40(5):1224–1244

    Google Scholar 

  20. Liu P, Guo JM, Wu CY, Cai D (2017) Fusion of deep learning and compressed domain features for content-based image retrieval. IEEE Trans Image Process 26(12):5706–5717

    Article  MathSciNet  MATH  Google Scholar 

  21. Ye F, Xiao H, Zhao X, Dong M, Luo W, Min W (2018) Remote sensing image retrieval using convolutional neural network features and weighted distance. IEEE Geosci Remote Sens Lett 15(10):1535–1539

    Article  Google Scholar 

  22. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Content-based brain tumor retrieval for MR images using transfer learning. IEEE Access 7:17809–17822

    Article  Google Scholar 

  23. Qayyum A, Anwar SM, Awais M, Majid M (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20

    Article  Google Scholar 

  24. Noel C, Osindero S, Dogwild! Distributed Hogwild for CPU & GPU

    Google Scholar 

  25. Elephas: distributed deep learning with Keras & Spark, https://github.com/max-pumperla/elephas

  26. Wang JZ (2020) Modeling objects, concepts, aesthetics and emotionsin big visual data. http://wang.ist.psu.edu/docs/home.shtml. Accessed 19 Nov 2020

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, Telangana State, 506004, India

    U. S. N. Raju, Debanjan Pathak, Harika Ala, Netalkar Rohan Kishor & Hillol Barman

Authors
  1. U. S. N. Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debanjan Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harika Ala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Netalkar Rohan Kishor
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hillol Barman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to U. S. N. Raju .

Editor information

Editors and Affiliations

  1. Department of Information Technology, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

    Rajesh Doriya

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

    Badal Soni

  3. Indian Institute of Information Technology, Pune, India

    Anupam Shukla

  4. Faculty of Science and Forestry, School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Raju, U.S.N., Pathak, D., Ala, H., Kishor, N.R., Barman, H. (2023). Distributed Deep Learning for Content-Based Image Retrieval. In: Doriya, R., Soni, B., Shukla, A., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5868-7_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5867-0

  • Online ISBN: 978-981-19-5868-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation