Enhancing Object Recognition Through a Novel Adaptive Recognition Technique (ART) Framework
DOI:
https://doi.org/10.4108/eetismla.6798Keywords:
Adaptive Recognition Technique , Adaptive Resonance Theory, Machine Learning, Object Recognition, Recognition FrameworkAbstract
Object recognition is a critical capability in various computer vision applications, but traditional approaches often struggle with complex, real-world scenarios. This paper introduces a novel Adaptive Recognition Technique (ART) framework to enhance object recognition performance. The proposed ART framework leverages adaptive learning mechanisms to more accurately identify objects, even in the presence of variations in size, orientation, and environmental conditions. Through a series of experiments on benchmark datasets, the ART framework demonstrated significant improvements in recognition accuracy compared to existing methods. Key innovations include the integration of unsupervised feature learning, dynamic model adaptation, and ensemble-based decision making. The results suggest that the ART framework offers a promising approach to advancing the state-of-the-art in object recognition, with potential applications in areas such as autonomous vehicles, surveillance, and image analysis. Further research is underway to expand the capabilities of the ART framework.
Downloads
References
[1] Carpenter G, Grossberg S. Adaptive resonance theory. Boston University Center for Adaptive Systems and Department of Cognitive …; 1998.
[2] Alam MS, Yakopcic C, Subramanyam G, Taha TM. Memristor based neuromorphic adaptive resonance theory for one-shot online learning and network intrusion detection. In: International Conference on Neuromorphic Systems 2020. 2020. p. 1–8.
[3] Bartfai G. An Adaptive Resonance Theory-based Neural Network for Autonomous Learning via Iterative Knowledge Redescription. In: 2021 International Joint Conference on Neural Networks (IJCNN). IEEE; 2021. p. 1–8.
[4] Barton A, Volna E, Kotyrba M. Control of autonomous robot behavior using data filtering through adaptive resonance theory. Vietnam Journal of Computer Science. 2018;5:85–94.
[5] Bargsten V, Kirchner F. Actuator-level motion and contact episode learning and classification using adaptive resonance theory. Intell Serv Robot. 2023;16(5):537–48.
[6] Dmitriy R. Developing a modified neural network of adaptive resonance theory for solving image recognition problems. 2016;
[7] Buhanov D, Chernikov S, Polyakov V, Panchenko M. Detection of Video Image Modification Using a Classifier Based on Adaptive Resonance Theory. In: International Scientific and Practical Conference on Information Technologies and Intelligent Decision Making Systems. Springer; 2022. p. 23–32.
[8] Jain C, Singh P, Rana A. Fuzzy logic based adaptive resonance Theory-1 approach for offline signature verification. Image Processing & Communications. 2017;22(3):23.
[9] Mukherjee S, Das A. Vague set theory based segmented image fusion technique for analysis of anatomical and functional images. Expert Syst Appl. 2020;159:113592.
[10] Leconte F, Ferland F, Michaud F. Fusion adaptive resonance theory networks used as episodic memory for an autonomous robot. In: Artificial General Intelligence: 7th International Conference, AGI 2014, Quebec City, QC, Canada, August 1-4, 2014 Proceedings 7. Springer; 2014. p. 63–72.
[11] Brahma A, Panigrahi S. Database intrusion detection using adaptive resonance network theory model. In: Computational Intelligence in Data Mining: Proceedings of the International Conference on ICCIDM 2018. Springer; 2020. p. 243–50.
[12] Rizvi S, Flock T, Flock T, Williams I. Anomaly Detection to Protect Networks from Advanced Persistent Threats Using Adaptive Resonance AI Concepts. In: 2020 International Conference on Software Security and Assurance (ICSSA). IEEE; 2020. p. 60–5.
[13] Tiwari P, Mishra P, Singh U, Itare R. New Adaptive Resonance Theory Based Intrusion Detection System. In: Second International Conference on Computer Networks and Communication Technologies: ICCNCT 2019. Springer; 2020. p. 745–54.
[14] Cao X, Yang J, Gao Y, Wang Q, Shen D. Region-adaptive deformable registration of CT/MRI pelvic images via learning-based image synthesis. IEEE Transactions on Image Processing. 2018;27(7):3500–12.
[15] Tripathi A, Singh KK, Srivastava G, Maurya PK. Analysis of Adaptive Resonance Theory for Diagnostic Understanding of Epilepsy. Book Rivers; 2022.
[16] Pourpanah F, Lim CP, Etemad A, Wu QMJ. An ensemble semi-supervised adaptive resonance theory model with explanation capability for pattern classification. IEEE Trans Emerg Top Comput Intell. 2023;
[17] Masuyama N, Nojima Y, Loo CK, Ishibuchi H. Multi-label classification via adaptive resonance theory-based clustering. IEEE Trans Pattern Anal Mach Intell. 2022;45(7):8696–712.
[18] Masuyama N, Nojima Y, Loo CK, Ishibuchi H. Multi-label classification based on adaptive resonance theory. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2020. p. 1913–20.
[19] Yamada Y, Masuyama N, Amako N, Nojima Y, Loo CK, Ishibuchi H. Divisive hierarchical clustering based on adaptive resonance theory. In: 2020 International Symposium on Community-centric Systems (CcS). IEEE; 2020. p. 1–6.
[20] Meng L, Tan AH, Wunsch D. Adaptive resonance theory in social media data clustering. Springer; 2019.
[21] Sevcenko N, Appel T, Ninaus M, Moeller K, Gerjets P. Theory-based approach for assessing cognitive load during time-critical resource-managing human–computer interactions: an eye-tracking study. Journal on Multimodal User Interfaces. 2023;17(1):1–19.
[22] Chiew FH, Ng CK, Chai KC, Tay KM. A fuzzy adaptive resonance theory‐based model for mix proportion estimation of high‐performance concrete. Computer‐Aided Civil and Infrastructure Engineering. 2017;32(9):772–86.
[23] da Silva LEB, Rayapati N, Wunsch DC. iCVI-ARTMAP: using incremental cluster validity indices and adaptive resonance theory reset mechanism to accelerate validation and achieve multiprototype unsupervised representations. IEEE Trans Neural Netw Learn Syst. 2022;
[24] Grossberg S. A path toward explainable AI and autonomous adaptive intelligence: deep learning, adaptive resonance, and models of perception, emotion, and action. Front Neurorobot. 2020;14:533355.
[25] Hamadache M, Jung JH, Park J, Youn BD. A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: Shallow and deep learning. JMST Advances. 2019;1:125–51.
[26] Kinoshita T, Masuyama N, Liu Y, Nojima Y, Ishibuchi H. Reference vector adaptation and mating selection strategy via adaptive resonance theory-based clustering for many-objective optimization. IEEE Access. 2023;11:126066–86.
[27] Kwon M, Lee SH, Ahn WY. Adaptive design optimization as a promising tool for reliable and efficient computational fingerprinting. Biol Psychiatry Cogn Neurosci Neuroimaging. 2023;8(8):798–804.
[28] Wang C, Fan H, Wu T. Novel rough set theory-based method for epistemic uncertainty modeling, analysis and applications. Appl Math Model. 2023;113:456–74.
[29] Wu W, Hu Y, Xu K, Qin L, Yin Q. Self-Organizing Memory Based on Adaptive Resonance Theory for Vision and Language Navigation. Mathematics. 2023;11(19):4192.
[30] Yao Q, Yang S, Shao Q, Bian C, Wu M. Topological clustering particle swarm optimizer based on adaptive resonance theory for multimodal multi-objective problems. Inf Sci (N Y). 2024;121106.
[31] Zhang C, Jiang C, Xu Q. Pretrained back propagation based adaptive resonance theory network for adaptive learning. J Algorithm Comput Technol. 2023;17:17483026231205008.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Hewa Majeed Zangana, Firas Mahmood Mustafa, Marwan Omar
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
