Fuzzy adaptive learning control network (FALCN) for image clustering and content-based image retrieval on noisy dataset

S. Neelakandan; Sathishkumar Veerappampalayam Easwaramoorthy; A. Chinnasamy; Jaehyuk Cho; S. Neelakandan; Sathishkumar Veerappampalayam Easwaramoorthy; A. Chinnasamy; Jaehyuk Cho

doi:10.3934/math.2023931

AIMS Mathematics

2023, Volume 8, Issue 8: 18314-18338. doi: 10.3934/math.2023931

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Fuzzy adaptive learning control network (FALCN) for image clustering and content-based image retrieval on noisy dataset

S. Neelakandan ¹,
Sathishkumar Veerappampalayam Easwaramoorthy ²,
A. Chinnasamy ³,
Jaehyuk Cho ^{2
,
,}

1.
Department of CSE, R.M.K. Engineering College, Chennai, 601206, India
2.
Department of Software Engineering, Jeonbuk National University, Jeonju-si, Jeollabuk-do, 54896, Republic of Korea
3.
Department of Data Science and Business Systems, School of Computing, SRMIST Kattankulathur, Chennai, 603023, India

Received: 15 February 2023 Revised: 03 May 2023 Accepted: 06 May 2023 Published: 29 May 2023
MSC : 65K10, 90C26, 90C52

It has been demonstrated that fuzzy systems are beneficial for classification and regression. However, they have been mainly utilized in controlled settings. An image clustering technique essential for content-based picture retrieval in big image datasets is developed using the contents of color, texture and shape. Currently, it is challenging to label a huge number of photos. The issue of unlabeled data has been addressed. Unsupervised learning is used. K-means is the most often used unsupervised learning algorithm. In comparison to fuzzy c-means clustering, K-means clustering has lower-dimensional space resilience and initialization resistance. The dominating triple HSV space was shown to be a perceptual color space made of three modules, S (saturation), H (hue) and V (value), referring to color qualities that are significantly connected to how human eyes perceive colors. A deep learning technique for segmentation (RBNN) is built on the Gaussian function, fuzzy adaptive learning control network (FALCN), clustering and the radial basis neural network. The segmented image and critical information are fed into a radial basis neural network classifier. The suggested fuzzy adaptive learning control network (FALCN) fuzzy system, also known as the unsupervised fuzzy neural network, is very good at clustering images and can extract image properties. When a conventional fuzzy network system receives a noisy input, the number of output neurons grows needlessly. Finally, random convolutional weights extract features from data without labels. Furthermore, the state-of-the-art uniting the proposed FALCN with the RBNN classifier, the proposed descriptor also achieves comparable performance, such as improved accuracy is 96.547 and reduced mean squared error of 36.028 values for the JAFE, ORL, and UMIT datasets.
- Fuzzy Adaptive Learning Control Network (FALCN),
- image clustering,
- content-based image retrieval,
- Radial Basis Neural Network (RBNN)
Citation: S. Neelakandan, Sathishkumar Veerappampalayam Easwaramoorthy, A. Chinnasamy, Jaehyuk Cho. Fuzzy adaptive learning control network (FALCN) for image clustering and content-based image retrieval on noisy dataset[J]. AIMS Mathematics, 2023, 8(8): 18314-18338. doi: 10.3934/math.2023931

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Abstract

It has been demonstrated that fuzzy systems are beneficial for classification and regression. However, they have been mainly utilized in controlled settings. An image clustering technique essential for content-based picture retrieval in big image datasets is developed using the contents of color, texture and shape. Currently, it is challenging to label a huge number of photos. The issue of unlabeled data has been addressed. Unsupervised learning is used. K-means is the most often used unsupervised learning algorithm. In comparison to fuzzy c-means clustering, K-means clustering has lower-dimensional space resilience and initialization resistance. The dominating triple HSV space was shown to be a perceptual color space made of three modules, S (saturation), H (hue) and V (value), referring to color qualities that are significantly connected to how human eyes perceive colors. A deep learning technique for segmentation (RBNN) is built on the Gaussian function, fuzzy adaptive learning control network (FALCN), clustering and the radial basis neural network. The segmented image and critical information are fed into a radial basis neural network classifier. The suggested fuzzy adaptive learning control network (FALCN) fuzzy system, also known as the unsupervised fuzzy neural network, is very good at clustering images and can extract image properties. When a conventional fuzzy network system receives a noisy input, the number of output neurons grows needlessly. Finally, random convolutional weights extract features from data without labels. Furthermore, the state-of-the-art uniting the proposed FALCN with the RBNN classifier, the proposed descriptor also achieves comparable performance, such as improved accuracy is 96.547 and reduced mean squared error of 36.028 values for the JAFE, ORL, and UMIT datasets.

References

[1]	C. Zhang, CNN‐VWII: An efficient approach for large‐scale video retrieval by image queries, Pattern Recogn. Lett., 123 (2019), 82–88. https://doi.org/10.1016/j.patrec.2019.03.015 doi: 10.1016/j.patrec.2019.03.015
[2]	A. Shinde, A. Rahulkar, C. Patil, Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor, Biomed. Eng. Lett., 9 (2019), 387–394. https://doi.org/10.1007/s13534-019-00112-0 doi: 10.1007/s13534-019-00112-0
[3]	P. Subbulakshmi, M. Prakash, Mitigating eavesdropping by using fuzzy based mdpop-q learning approach and multilevel Stackelberg game theoretic approach in wireless CRN, Cogn. Syst. Res., 52 (2018), 853–861. https://doi.org/10.1016/j.cogsys.2018.09.021 doi: 10.1016/j.cogsys.2018.09.021
[4]	M. Indu, K. V. Kavitha, Survey on sketch-based image retrieval methods, International Conference on Circuit, Power, and Computing Technologies (ICCPCT), Nagercoil, India, 2016, 1–4. https://doi.org/10.1109/ICCPCT.2016.7530358
[5]	S. K. Panigrahi, S. Gupta, P. K. Sahu, Curvelet‐based multiscale denoising using non‐local means & guided image filter, IET Image Process., 12 (2018), 909–918. https://doi.org/10.1049/iet-ipr.2017.0825 doi: 10.1049/iet-ipr.2017.0825
[6]	J. Xu, L. Yun, X. Zheng, Forensic detection of Gaussian low pass filtering in digital images, Proceedings of the 8th International Congress on Image and Signal Processing (CISP), Shenyang, China, 2015,819–823. https://doi.org/10.1109/CISP.2015.7407990
[7]	Y. F. Lu, Extended biologically inspired model for object recognition based on oriented Gaussian–Hermite moment, Neurocomputing, 139 (2014), 189–201. https://doi.org/10.1016/j.neucom.2014.02.046 doi: 10.1016/j.neucom.2014.02.046
[8]	S. Wang, J. Zhang, T. X. Han, Z. Miao, Z. Miao, Sketch-based image retrieval through hypothesis-driven object boundary selection with HLR descriptor, IEEE T. Multimedia, 7 (2015), 1045–1057. https://doi.org/10.1109/TMM.2015.2431492 doi: 10.1109/TMM.2015.2431492
[9]	R. Mandal, P. P. Roy, U. Pal, M. Blumenstein, Bag-of-visual-words for signature-based multi-script document retrieval, Neural Comput. Appl., 31 (2019), 6223–6247. https://doi.org/10.1007/s00521-018-3444-y doi: 10.1007/s00521-018-3444-y
[10]	H. Dawood, M. H. Alkinani, A. Raza, H. Dawood, R. Mehboob, S. Shabbir, Correlated microstructure descriptor for image retrieval, IEEE Access, 7 (2019), 55206–55228. https://doi.org/10.1109/ACCESS.2019.2911954 doi: 10.1109/ACCESS.2019.2911954
[11]	M. N. Sharath, T. M. Rajesh, M. Patil, Design of optimal metaheuristics based pixel selection with homomorphic encryption technique for video steganography, Int. J. Inf. Tecnol. 14 (2022), 2265–2274. https://doi.org/10.1007/s41870-022-01005-9 doi: 10.1007/s41870-022-01005-9
[12]	P. Mohan, M. Subramanian, V. A. Sambath, Gradient Boosted Decision Tree-Based Influencer Prediction in Social Network Analysis, Big Data Cogn. Comput. 7 (2023), 6. https://doi.org/10.3390/bdcc7010006 doi: 10.3390/bdcc7010006
[13]	Z. Zhao, X. Li, B. Luan, W. Jiang, W. Gao, S. Neelakandan, Secure Internet of Things (IoT) using a Novel Brooks Iyengar Quantum Byzantine Agreement-centered lockchain Networking (BIQBA-BCN) Model in Smart Healthcare, Inf. Sci., 629 (2023), 440–455. https://doi.org/10.1016/j.ins.2023.01.020 doi: 10.1016/j.ins.2023.01.020
[14]	T. Veeramani, S. Bhatia, Fida Hussain Memon, Design of fuzzy logic-based energy management and traffic predictive model for cyber physical systems, Comput. Electr. Eng., 102 (2022), 108135, https://doi.org/10.1016/j.compeleceng.2022.108135. doi: 10.1016/j.compeleceng.2022.108135
[15]	I. Couso, C. Borgelt, E. Hüllermeier, Rudolf Kruse, Fuzzy sets in data analysis, From statistical foundations to machine learning, IEEE Comput. Intell. Mag., 14 (2019), 31–44. https://doi.org/10.1109/MCI.2018.2881642 doi: 10.1109/MCI.2018.2881642
[16]	Q. Wang, X. Wang, C. Fang, W. Yang, Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation, Appl. Soft Comput., 92 (2020), 106318. https://doi.org/10.1016/j.asoc.2020.106318 doi: 10.1016/j.asoc.2020.106318
[17]	S. Satpathy, M. Prakash, S. Debbarma, A. S. Sengupta, B. K. Bhattacaryya, Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area, J. Intell. Fuzzy Syst., 37 (2019), 7039–7046. https://doi.org/10.3233/JIFS-181577 doi: 10.3233/JIFS-181577
[18]	N. Ali, B. Zafar, F. Riaz, A hybrid geometric spatial image representation for scene classification, PLoS One, 13 (2018), 203339. https://doi.org/10.1371/journal.pone.0203339 doi: 10.1371/journal.pone.0203339
[19]	S. G. Sanu, P. S. Tamase, Satellite image mining using content-based image retrieval, Int. J. Eng. Sci, 14 (2017), 13928.
[20]	P. Desai, J. Pujari, N. H. Ayachit, Classification of Archaeological Monuments for Different Art forms with an Application to CBIR, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Mysore, India, 2013, 1108–1112. https://doi.org/10.1109/ICACCI.2013.6637332
[21]	D. Chandraprakash, M. Narayana, Content based satellite cloud image retrieval and rainfall estimation using shape features, SSRG-IJGGS, 4 (2017), 34–39. https://doi.org/10.14445/23939206/IJGGS-V4I2P105 doi: 10.14445/23939206/IJGGS-V4I2P105
[22]	P. Pattanasethanon1, B. Attachoo, An alternative approach for unsupervised cluster-based image retrieval, Int. J. Phy. Sci., 7 (2021), 5498–5510. https://doi.org/10.5897/IJPS11.1287 doi: 10.5897/IJPS11.1287
[23]	B. Zafar, R. Ashraf, N. Ali, M. Ahmed, S. Jabbar, S. A. Chatzichristofis, Image classification by addition of spatial information based on histograms of orthogonal vectors, PLoS One, 13 (2018), 0198175. https://doi.org/10.1371/journal.pone.0198175 doi: 10.1371/journal.pone.0198175
[24]	N. Hor, F. E. Shervan, Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information, 2019. https://doi.org/10.48550/arXiv.1912.12978
[25]	N. T. Bani, S. Fekri-Ershad, Content-based image retrieval based on combination of texture and colour information extracted in spatial and frequency domains, Electron. Libr., 37 (2019), 650–666. https://doi.org/10.1108/EL-03-2019-0067 doi: 10.1108/EL-03-2019-0067
[26]	H. Dawood, M. H. Alkinani, A. Raza, H. Dawood, R. Mehboob, S. Shabbir, Correlated microstructure descriptor for image retrieval, IEEE Access, 7 (2019), 55206–55228. https://doi.org/10.1109/ACCESS.2019.2911954 doi: 10.1109/ACCESS.2019.2911954
[27]	Y. Mistry, D. Ingole, M. Ingole, Content based image retrieval using hybrid features and various distance metric, J. Electr. Syst. Inf. Technol., 5 (2017), 878–888. https://doi.org/10.1016/j.jesit.2016.12.009 doi: 10.1016/j.jesit.2016.12.009
[28]	Y. Duan, J. Lu, J. Feng, J. Zhou, Context-aware local binary feature learning for face recognition, IEEE T. Pattern Anal. Mach. Intell., 40 (2018), 1139–1153. https://doi.org/10.1109/TPAMI.2017.2710183 doi: 10.1109/TPAMI.2017.2710183
[29]	B. Ferreira, J. Rodrigues, J. Leitao, H. Domingos, Practical privacy-preserving content-based retrieval in cloud image repositories, IEEE T. Cloud Comput., 7 (2017), 784–798. https://doi.org/10.1109/TCC.2017.2669999 doi: 10.1109/TCC.2017.2669999
[30]	V. A. Kumar, Coalesced global and local feature discrimination for content-based image retrieval, Int. J. Inf. Technol., 9 (2017), 431–446. https://doi.org/10.1007/s41870-017-0042-7 doi: 10.1007/s41870-017-0042-7
[31]	V. Sambath, R. A. M. Ramanujam, M. Sammeta, Deep learning enabled cross-lingual search with metaheuristic web-based query optimization model for multi-document summarization, Concurr. Comput. Pract. Exper., 35 (2022), e7476. https://doi.org/10.1002/cpe.7476 doi: 10.1002/cpe.7476
[32]	P. Srivastava, A. Khare, Utilizing multiscale local binary pattern for content-based image retrieval, Multimed. Tools Appl., 77 (2018), 12377–12403. https://doi.org/10.1007/s11042-017-4894-4 doi: 10.1007/s11042-017-4894-4
[33]	M. Yousuf, Z. Mehmood, H. A. Habib, T. Mahmood, T. Saba, A. Rehman, et al., A novel technique based on visual words fusion analysis of sparse features for effective content-based image retrieval, Math. Probl. Eng., 2018 (2018), 2134395. https://doi.org/10.1155/2018/2134395 doi: 10.1155/2018/2134395
[34]	Z. Mehmood, N. Gul, M. Altaf, T. Mahmood, T. Saba, A. Rehman, et al., Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model, J. Image Video Proc., 2018 (2018), 48. https://doi.org/10.1186/s13640-018-0285-7 doi: 10.1186/s13640-018-0285-7
[35]	L. Lei, C. Wu, X. Tian, Robust deep kernel-based fuzzy clustering with spatial information for image segmentation, Appl. Intell., 53 (2023), 23–48. https://doi.org/10.1007/s10489-022-03255-3 doi: 10.1007/s10489-022-03255-3
[36]	L. Guo, P. Shi, L. Chen, C. Chen, W. Ding, Pixel and region level information fusion in membership regularized fuzzy clustering for image segmentation, Inform. Fusion, 92 (2023), 479–497. https://doi.org/10.1016/j.inffus.2022.12.008 doi: 10.1016/j.inffus.2022.12.008
[37]	A. Nazir, R. Ashraf, T. Hamdani, N. Ali, Content based image retrieval system by using HSV color histogram, discrete wavelet transforms and edge histogram descriptor, 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)., Sukkur, Pakistan, 2018, 1–6. https://doi.org/10.1109/ICOMET.2018.8346343
[38]	P. Liu, J. M. Guo, K. Chamnongthai, H. Prasetyo, Fusion of color histogram and LBP-based features for texture image retrieval and classification, Inf. Sci., 390 (2017), 95–111. https://doi.org/10.1016/j.ins.2017.01.025 doi: 10.1016/j.ins.2017.01.025
[39]	K. T. Ahmed, M. A. Iqbal, A. Iqbal, Content based image retrieval using image features information fusion, Inform. Fusion, 51 (2018), 76–99. https://doi.org/10.1016/j.inffus.2018.11.004 doi: 10.1016/j.inffus.2018.11.004
[40]	N. Subramani, A. Mardani, P. Mohan, A. R. Mishra, P. Ezhumalai, A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks, AIMS Mathematics, 8 (2023), 8310–8331. https://doi.org/10.3934/math.2023419 doi: 10.3934/math.2023419
[41]	C. J. Lin, C. T. Lin, An ART-based fuzzy adaptive learning control network, IEEE T. Fuzzy Syst., 5 (1997), 477–496. https://doi.org/10.1109/91.649900 doi: 10.1109/91.649900

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