The Development of Residual Network (ResNet-18) Convolutional Neural Network (CNN) Architecture Combined with Content-Based Image Retrieval (CBIR) Method to Measure Logo Image Similarity Level

Authors

  • Larissa Navia Rani Information System, Universitas Putra Indonesia YPTK Padang, Lubuk Begalung Highway, Padang, 25221, Indonesia
  • Yuhandri Information Technology, Universitas Putra Indonesia YPTK Padang, Lubuk Begalung Highway, Padang, 25221, Indonesia
  • Muhammad Tajuddin Information Technology, Universitas Putra Indonesia YPTK Padang, Lubuk Begalung Highway, Padang, 25221, Indonesia.

Keywords:

ResNet 18, CNN, CBIR, Logo Image, Similarity Level

Abstract

In this study, the degree of visual resemblance between two logos—both those that were unique and those that were identical—will be measured. It can be done by compiling a database of logo images from various sources of existing logo image data that have been stored and extracted. In this study, four brand pictures serve as data testing while 210 photo data for the database serve as data training. All of the logo images were provided by the West Sumatera Regional Office of the Ministry of Law and Human Rights of the Republic of Indonesia (Kemenkumham—Kementerian Hukum dan Hak Asasi Manusia—Republik Indonesia). The 320 by 320 color size photos guarantee the most precise dimensional uniformity technique for the images. Instead of using the Residual Network (ResNet-18) Architecture of the Convolutional Neural Network (CNN) type, the Content-Based Image Retrieval (CBIR) approach was employed to generate a suitable similarity score for the research. This approach automatically distributes training images and validation images, using 147 training image data values (70%) and 63 validation images (30%) out of the 210 available photos. As a result of this research, an algorithm that measures logo picture similarity will be created and used in method implementation and tool software. This tool has a 93.65% accuracy rate after 84 iterations.

Downloads

Download data is not yet available.

References

K. Kumar Jakkur Patalappa and S. Maganahalli Chandramouli, “Robust recognition of logo under makeover in the context of content piracy,” Glob. Transitions Proc., vol. 2, no. 2, pp. 421–428, 2021, doi: 10.1016/j.gltp.2021.08.050.

T. Ghosh, S. Sreejesh, and Y. K. Dwivedi, “Brand logos versus brand names: A comparison of the memory effects of textual and pictorial brand elements placed in computer games,” J. Bus. Res., vol. 147, no. March, pp. 222–235, 2022, doi: 10.1016/j.jbusres.2022.04.017.

P. de Lencastre, J. C. Machado, and P. Costa, “The Effect of Brand Names and Logos’ Figurativeness on Memory an Experimental Approach,” SSRN Electron. J., vol. 164, no. February 2022, 2022, doi: 10.2139/ssrn.4052517.

B. R. Estevam and D. M. Riaño-Pachón, “CoCoView - A codon conservation viewer via sequence logos,” MethodsX, vol. 9, p. 101803, 2022, doi: 10.1016/j.mex.2022.101803.

J. Majeed Sadeq, B. Aziz Qadir, and H. Hassan Abbas, “Cars logo recognition by using of backpropagation neural networks,” Meas. Sensors, vol. 26, no. January, p. 100702, 2023, doi: 10.1016/j.measen.2023.100702.

X. Gao and W. Chen, “Brand LOGO Image Recognition and Inquiry Creative Teaching Design Based on Deep Learning,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/5396468.

X. Jia, Y. Dong, and B. Wang, “Logo Design Process and Method of Intellectual Property Big Data in the Digital Media Era,” Discret. Dyn. Nat. Soc., vol. 2022, no. 1, 2022, doi: 10.1155/2022/2556365.

N. Tian, Y. Liu, Z. Sun, and X. Liu, “Clustering-and Transformer-Based Networks for the Style Analysis of Logo Images,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/2090712.

H. Zheng, “False Vision Graphics in Logo Design Based on Artificial Intelligence in the Visual Paradox Environment,” J. Environ. Public Health, vol. 2022, 2022, doi: 10.1155/2022/1832083.

L. Kannappan, S. K. Palaniswamy, M. Kanagasabai, and S. Kumar, “A Car Logo Design-Inspired CPW-Fed Semitransparent Antenna for Vehicular Applications,” Int. J. Antennas Propag., vol. 2023, 2023, doi: 10.1155/2023/7101049.

C. P. Leal, J. P. Lis-Gutiérrez, C. Henao-Rodríguez, and M. Lis-Gutiérrez, “Determinants of the trademark in Colombia: A panel data application,” Procedia Comput. Sci., vol. 175, pp. 120–126, 2020, doi: 10.1016/j.procs.2020.07.020.

Suslina and P. Mineeva, “Use of Digital Technologies for Optimizing the Handling of Trademark Applications,” Procedia Comput. Sci., vol. 169, no. 2019, pp. 435–439, 2020, doi: 10.1016/j.procs.2020.02.242.

F. Mohd Anuar, R. Setchi, and Y. K. Lai, “A conceptual model of trademark retrieval based on conceptual similarity,” Procedia Comput. Sci., vol. 22, pp. 450–459, 2013, doi: 10.1016/j.procs.2013.09.123.

P. Szwajdler, “Limitations of the Freedom of Hyperlinking in the Fields of Copyright Law, Trademark Law and Unfair Competition Law: Is Case-by-case Approach Sufficient?,” Comput. Law Secur. Rev., vol. 45, no. April 2016, pp. 1–16, 2022, doi: 10.1016/j.clsr.2022.105692.

F. Kaiser, A. Cuntz, and C. Peukert, “Batman forever? The role of trademarks for reuse in the US comics industry,” Res. Policy, vol. 52, no. 8, p. 104820, 2023, doi: 10.1016/j.respol.2023.104820.

V. I. Nitu, “Comparative Analysis of the Community Trademark Registration Activity of Germany, the United States and China,” Procedia Econ. Financ., vol. 15, no. 14, pp. 603–608, 2014, doi: 10.1016/s2212-5671(14)00528-0.

E. R. S. de Rezende, G. C. S. Ruppert, A. Theóphilo, E. K. Tokuda, and T. Carvalho, “Exposing computer generated images by using deep convolutional neural networks,” Signal Process. Image Commun., vol. 66, pp. 113–126, 2018, doi: 10.1016/j.image.2018.04.006.

Mahajan and S. Chaudhary, “Categorical Image Classification Based on Representational Deep Network (RESNET),” Proc. 3rd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2019, pp. 327–330, 2019, doi: 10.1109/ICECA.2019.8822133.

Y. Li, Q. Shi, J. Deng, and F. Su, “Graphic logo detection with deep region-based convolutional networks,” 2017 IEEE Vis. Commun. Image Process. VCIP 2017, vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/VCIP.2017.8305065.

M. Sharma, B. Jain, C. Kargeti, V. Gupta, and D. Gupta, “Detection and Diagnosis of Skin Diseases Using Residual Neural Networks (RESNET),” Int. J. Image Graph., vol. 21, no. 5, 2021, doi: 10.1142/S0219467821400027.

Golzari Oskouei and M. Hashemzadeh, “CGFFCM: A color image segmentation method based on cluster-weight and feature-weight learning[Formula presented],” Softw. Impacts, vol. 11, no. December 2021, p. 100228, 2022, doi: 10.1016/j.simpa.2022.100228.

S. G. Sodjinou, V. Mohammadi, A. T. Sanda Mahama, and P. Gouton, “A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images,” Inf. Process. Agric., vol. 9, no. 3, pp. 355–364, 2022, doi: 10.1016/j.inpa.2021.08.003.

S. Bhattacharya, L. Bennet, J. O. Davidson, and C. P. Unsworth, “Ordered iterative colour channel selection enhances segmentation of brain slice image neurons of the sham fetal sheep model,” Intell. Syst. with Appl., vol. 15, no. July, p. 200104, 2022, doi: 10.1016/j.iswa.2022.200104.

S. Du, K. Luo, Y. Zhi, H. Situ, and J. Zhang, “Binarization of grayscale quantum image denoted with novel enhanced quantum representations,” Results Phys., vol. 39, no. March, p. 105710, 2022, doi: 10.1016/j.rinp.2022.105710.

B. J. Bipin Nair, K. Aadith Raj, M. Kedar, S. P. Vaishak, and E. Sreejil, “Ancient Epic Manuscript Binarization and Classification Using False Color Spectralization and VGG-16 Model,” Procedia Comput. Sci., vol. 218, pp. 631–643, 2023, doi: 10.1016/j.procs.2023.01.045.

R. Gutierrez-Bonet et al., “Analysis of Choroidal Vascularity Index in Keratoconus Patients Using Swept-Source Optical Coherence Tomography-Based Binarization Techniques,” J. Ophthalmol., vol. 2020, 2020, doi: 10.1155/2020/1682463.

J. Chen, L. Chen, and M. Shabaz, “Image Fusion Algorithm at Pixel Level Based on Edge Detection,” J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/5760660.

H. Li, “Multilevel Image Edge Detection Algorithm Based on Visual Perception,” Secur. Commun. Networks, vol. 2022, 2022, doi: 10.1155/2022/3502041.

X. You, G. Yan, and Z. Yang, “Intelligent Edge Computing Detection Vehicle and Detection Method Based on Tunnel Lining Concrete,” Int. Trans. Electr. Energy Syst., vol. 2022, 2022, doi: 10.1155/2022/1837800.

D. Hu, H. Yang, and X. Hou, “Distance Field-Based Convolutional Neural Network for Edge Detection,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/1712258.

P. Flores-Vidal, J. Castro, and D. Gómez, “Postprocessing of Edge Detection Algorithms with Machine Learning Techniques,” Math. Probl. Eng., vol. 2022, 2022, doi: 10.1155/2022/9729343.

T. Leskovar and M. Cre, “Forensic Science International : Genetics Kinship analysis of 5th- to 6th-century skeletons of Romanized indigenous people from the Bled – Pristava archaeological site ˇ ˇ Miriam Ger ˇ,” vol. 65, no. March, 2023, doi: 10.1016/j.fsigen.2023.102886.

L. Guan, X. Zhao, Z. Ji, M. Jiang, Y. Cui, and L. Weng, “Preparation of 3D BN-BT / PVDF skeleton structure composites for high thermal conductivity and energy storage,” Polym. Test., vol. 125, no. June, p. 108126, 2023, doi: 10.1016/j.polymertesting.2023.108126.

M. Clémot and J. Digne, “Computers & Graphics Neural skeleton : Implicit neural representation away from the surface,” Comput. Graph., vol. 114, pp. 368–378, 2023, doi: 10.1016/j.cag.2023.06.012.

S. Sardari et al., “Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review,” Comput. Biol. Med., vol. 158, no. March, 2023, doi: 10.1016/j.compbiomed.2023.106835.

Prof. Bhushan Thakre, Dr. R.M Thakre. (2017). Analysis of Modified Current Controller and its Implementation in Automotive LED. International Journal of New Practices in Management and Engineering, 6(04), 01 - 06. https://doi.org/10.17762/ijnpme.v6i04.60

Rajendra, K. ., Subramanian, S. ., Karthik, N. ., Naveenkumar, K. ., & Ganesan, S. . (2023). Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 59–67. https://doi.org/10.17762/ijritcc.v11i2s.6029

Raghavendra, S., Dhabliya, D., Mondal, D., Omarov, B., Sankaran, K. S., Dhablia, A., . . . Shabaz, M. (2022). Development of intrusion detection system using machine learning for the analytics of internet of things enabled enterprises. IET Communications, doi:10.1049/cmu2.12530

Downloads

Published

16.07.2023

How to Cite

Rani, L. N. ., Yuhandri, & Tajuddin, M. . (2023). The Development of Residual Network (ResNet-18) Convolutional Neural Network (CNN) Architecture Combined with Content-Based Image Retrieval (CBIR) Method to Measure Logo Image Similarity Level. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 1177–1189. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3377

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.