Cover
Vol. 19 No. 2 (2023)

Published: December 31, 2023

Pages: 145-157

Original Article

Content-Based Image Retrieval using Hard Voting Ensemble Method of Inception, Xception, and Mobilenet Architectures

Meqdam A. Mohammed 1 Zakariya A. Oraibi Mohammed Abdulridha Hussain
1Department of Computer Science, College of Education for Pure Sciences, University of Basrah, Basrah 61004, Iraq
History
  • Received: May 1, 2023
  • Revised: July 9, 2023
  • Accepted: July 19, 2023
  • Available Online: August 15, 2023
DOI

https://doi.org/10.37917/ijeee.19.2.17

Abstract

Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low- level attributes and human visual perception has become pivotal in refining Content Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.

Keywords

References

  1. X. Li, J. Yang, and J. Ma, “Recent developments of content-based image retrieval (cbir),” Neurocomputing, vol. 452, pp. 675–689, 2021.
  2. L. Tang, J. Yuan, and J. Ma, “Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network,” Information Fusion, vol. 82, pp. 28–42, 2022.
  3. P. Srivastava and A. Khare, “Integration of wavelet trans- form, local binary patterns and moments for content- based image retrieval,” Journal of Visual Communica- tion and Image Representation, vol. 42, pp. 78–103, 2017.
  4. X. Zhang, H. Zhai, J. Liu, Z. Wang, and H. Sun, “Real- time infrared and visible image fusion network using 156 | Mohammed, Oraibi & Hussain adaptive pixel weighting strategy,” Information Fusion, p. 101863, 2023.
  5. Y. Liu, X.-Y. Zhang, J.-W. Bian, L. Zhang, and M.-M. Cheng, “Samnet: Stereoscopically attentive multi-scale network for lightweight salient object detection,” IEEE Transactions on Image Processing, vol. 30, pp. 3804– 3814, 2021.
  6. M. J. J. Ghrabat, G. Ma, I. Y. Maolood, S. S. Al- resheedi, and Z. A. Abduljabbar, “An effective image retrieval based on optimized genetic algorithm utilized a novel svm-based convolutional neural network classifier,” Human-centric Computing and Information Sciences, vol. 9, pp. 1–29, 2019.
  7. S. G. More and I. Mohammed, “Survey on cbir using k- secure sum protocol in privacy preserving framework’,” International Journal of Computer Science and Informa- tion Security, IJCSIS, pp. 184–188, 2015.
  8. M. Yousuf, Z. Mehmood, H. A. Habib, T. Mahmood, T. Saba, A. Rehman, M. Rashid, and etal, “A novel tech- nique based on visual words fusion analysis of sparse fea- tures for effective content-based image retrieval,” Math- ematical Problems in Engineering, vol. 2018, 2018.
  9. K. Meethongjan, M. Dzulkifli, A. Rehman, A. Al- tameem, and T. Saba, “An intelligent fused approach for face recognition,” Journal of Intelligent Systems, vol. 22, no. 2, pp. 197–212, 2013.
  10. G. Acar, M. Juarez, N. Nikiforakis, C. Diaz, S. G¨urses, F. Piessens, and B. Preneel, “Fpdetective: dusting the web for fingerprinters,” in Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security, pp. 1129–1140, 2013.
  11. E. Balsa, C. Troncoso, and C. Diaz, “Ob-pws: Obfuscation-based private web search,” in 2012 IEEE Symposium on Security and Privacy, pp. 491–505, IEEE, 2012.
  12. R. L. Lagendijk, Z. Erkin, and M. Barni, “Encrypted signal processing for privacy protection: Conveying the utility of homomorphic encryption and multiparty com- putation,” IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 82–105, 2012.
  13. G.-H. Liu, Z.-Y. Li, L. Zhang, and Y. Xu, “Image re- trieval based on micro-structure descriptor,” Pattern Recognition, vol. 44, no. 9, pp. 2123–2133, 2011.
  14. B. Zafar, R. Ashraf, N. Ali, M. K. Iqbal, M. Sajid, S. H. Dar, and N. I. Ratyal, “A novel discriminating and rela- tive global spatial image representation with applications in cbir,” Applied Sciences, vol. 8, no. 11, p. 2242, 2018.
  15. W. Wei and Y. Wang, “Color image retrieval based on quaternion and deep features,” IEEE Access, vol. 7, pp. 126430–126438, 2019.
  16. Z. Xia, X. Wang, L. Zhang, Z. Qin, X. Sun, and K. Ren, “A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing,” IEEE trans- actions on information forensics and security, vol. 11, no. 11, pp. 2594–2608, 2016.
  17. R. Khan, C. Barat, D. Muselet, C. Ducottet, et al., “Spa- tial orientations of visual word pairs to improve bag-of- visual-words model.,” in BMVC, pp. 1–11, 2012.
  18. Y. Song, I. V. McLoughlin, and L.-R. Dai, “Local coding based matching kernel method for image classification,” PloS one, vol. 9, no. 8, p. e103575, 2014.
  19. R. Ashraf, T. Mahmood, A. Irtaza, and K. Bajwa, “A novel approach for the gender classification through trained neural networks,” J. Basic Appl. Sci. Res, vol. 4, pp. 136–144, 2014.
  20. L.-J. Li, H. Su, Y. Lim, and L. Fei-Fei, “Object bank: An object-level image representation for high-level visual recognition,” International journal of computer vision, vol. 107, pp. 20–39, 2014.
  21. M. Zang, D. Wen, T. Liu, H. Zou, and C. Liu, “A pooled object bank descriptor for image scene classification,” Expert Systems with Applications, vol. 94, pp. 250–264, 2018.
  22. G. J. Scott, M. R. England, W. A. Starms, R. A. Marcum, and C. H. Davis, “Training deep convolutional neural networks for land–cover classification of high-resolution imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 4, pp. 549–553, 2017.
  23. G. J. Scott, R. A. Marcum, C. H. Davis, and T. W. Nivin, “Fusion of deep convolutional neural networks for land cover classification of high-resolution imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 9, pp. 1638–1642, 2017.
  24. Y. Xu, Q. Lin, J. Huang, and Y. Fang, “An improved ensemble-learning-based cbir algorithm,” in 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC), pp. 1–3, IEEE, 2020. 157 | Mohammed, Oraibi & Hussain
  25. Z. Huang, R. Wang, S. Shan, and X. Chen, “Projection metric learning on grassmann manifold with applica- tion to video based face recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 140–149, 2015.
  26. A. L. Lafta and A. I. Abdulsada, “Privacy-preserve content-based image retrieval using aggregated local features.,” Iraqi Journal for Electrical & Electronic En- gineering, vol. 18, no. 2, 2022.
  27. M. Karthikeyan and D. Raja, “Deep transfer learning en- abled densenet model for content based image retrieval in agricultural plant disease images,” Multimedia Tools and Applications, pp. 1–24, 2023.
  28. A. Mehbodniya, J. Webber, A. G. Devi, R. P. Somineni, M. C. Chinnaiah, A. Asokan, and K. S. Bhanu, “Content- based image recovery system with the aid of median binary design pattern.,” Traitement du Signal, vol. 40, no. 2, 2023.
  29. T. Gherbi, A. Zeggari, Z. A. Seghir, and F. Hachouf, “Entropy-guided assessment of image retrieval systems: Advancing grouped precision as an evaluation measure for relevant retrievability,” Informatica, vol. 47, no. 7, 2023.
  30. G. K. Raju, P. Padmanabham, and A. Govardhan, “En- hanced content-based image retrieval with trio-deep fea- ture extractors with multi-similarity function.,” Inter- national Journal of Intelligent Engineering & Systems, vol. 15, no. 6, 2022.
  31. B. Sreenivasulu, A. Pasala, and G. Vasanth, “Adaptive inception based on transfer learning for effective visual recognition,” International Journal of Intelligent Engi- neering and Systems, vol. 13, no. 6, pp. 1–10, 2020.
  32. X. Han, Z. Wu, Y.-G. Jiang, and L. S. Davis, “Learning fashion compatibility with bidirectional lstms,” in Pro- ceedings of the 25th ACM international conference on Multimedia, pp. 1078–1086, 2017.
  33. M. Yasmin, M. Sharif, and S. Mohsin, “Neural networks in medical imaging applications: A survey,” World Ap- plied Sciences Journal, vol. 22, no. 1, pp. 85–96, 2013.
  34. A. Jimenez, J. M. Alvarez, and X. Giro-i Nieto, “Class- weighted convolutional features for visual instance search,” arXiv preprint arXiv:1707.02581, 2017.
  35. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural net- works for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
  36. A. Mahabub, M. I. Mahmud, and M. F. Hossain, “A robust system for message filtering using an ensemble machine learning supervised approach,” ICIC Express Letters, Part B: Applications, vol. 10, no. 9, pp. 805–811, 2019.