IJBTT

The Research of Image Classification Methods Based on the Introducing Cluster Representation Parameters for the Structural Description

The Research of Image Classification Methods Based on the Introducing Cluster Representation Parameters for the Structural Description
	© 2021 by IJETT Journal
	Volume-69 Issue-10
	Year of Publication : 2021
	Authors : M. Ayaz Ahmad, Volodymyr Gorokhovatskyi, Iryna Tvoroshenko, Nataliia Vlasenko, Syed Khalid Mustafa
	DOI :  10.14445/22315381/IJETT-V69I10P223

How to Cite?

M. Ayaz Ahmad, Volodymyr Gorokhovatskyi, Iryna Tvoroshenko, Nataliia Vlasenko, Syed Khalid Mustafa, "The Research of Image Classification Methods Based on the Introducing Cluster Representation Parameters for the Structural Description," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 186-192, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P223

Abstract
The results of the development of high-speed methods for classifying images in computer vision systems using the description as a set of keypoints descriptors are presented. Classification methods based on the system of cluster centers parameters, which are independently constructed for the etalon descriptors set, are researched. The competitive voting of the descriptors of the object being recognized on a set of etalon centers is proposed. An optimal way of comparing the sets of cluster centers for an object and etalons is applied. Experimental estimation of the efficiency for the two presented classification methods in terms of computation time and classification accuracy based on the results of applied dataset processing are shown. Based on the research, a conclusion about the effectiveness of classification technologies using cluster centers for structural descriptions of images to ensure decision-making in real-time is made.

Keywords
Computer Vision, Descriptor, Image Classification, Keypoint, ORB Detector.

Reference
[1] R. O. Duda, P. E. Hart, and D. G. Stork, Unsupervised learning and clustering, in Pattern classification. Hoboken, New Jersey, USA: John Wiley & Sons. (2000) 517-580.
[2] X. Zhang, F. X. Yu, S. Karaman, and S. Chang, Learning Discriminative and Transformation Covariant Local Feature Detectors, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), (2017) 4923-4931.
[3] G. Sharma, and B. Schiele, Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), (2015) 1296-1304.
[4] V. A. Gorokhovatskiy, Compression of Descriptions in the Structural Image Recognition, Telecommun. Radio Eng. 70(15) (2011) 1363- 1371.
[5] V. A. Gorokhovatskiy, and T. V. Poliakova, Geometrical Invariant Features Peculiar for the Methods of Structural Classification of Images, Telecommun. Radio Eng. 71(17) (2012) 1557-1564.
[6] L. Shapiro, Computer vision. New Jersey, USA: Prentice Hall. (2001) 325-410.
[7] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, ORB: an efficient alternative to SIFT or SURF, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), (2011) 2564-2571.
[8] E. Karami, S. Prasad, and M. Shehata, Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images, (2017) arXiv: 1710.02726.
[9] M. A. Muthiah, E. Logashamugam, and N. M. Nandhitha, Performance evaluation of Googlenet, Squeezenet, and Resnet50 in the classification of Herbal images, Int. J. Eng. Trends Technol. 69(3) (2021) 229-232.
[10] V. O. Gorokhovatsky, and S. V. Gadetska, Determination of Relevance of Visual Object Images by Application of Statistical Analysis of Regarding Fragment Representation of their Descriptions, Telecommun. Radio Eng. 78(3) (2019) 211-220.
[11] T. Kohonen, Self-Organizing Maps. Heidelberg, Berlin: Springer- Verlag. (2001) 212-246.
[12] T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka, Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost, IEEE Trans. Pattern Anal. Mach. Intell. 35(11) (2013) 2624- 2637.
[13] A. Iscen, G. Tolias, P. Gosselin, and H. Jégou, A Comparison of Dense Region Detectors for Image Search and Fine-Grained Classification, IEEE Trans. Image Process. 24(8) (2015) 2369-2381.
[14] C. Celik, and H. Sakir, Content based image retrieval with sparse representations and local feature descriptors: A comparative study, Pattern Recognit. 68 (2017) 1-13.
[15] P. Flach, Machine learning. The Art and Science of Algorithms that Make Sense of Data. New York, NY, USA: Cambridge University Press. (2012) 298-329.
[16] V. ?. Gorokhovatskyi, I. S. Tvoroshenko, and N. V. Vlasenko, Using fuzzy clustering in structural methods of image classification, Telecommun. Radio Eng. 79(9) (2020) 781-791.
[17] I. Tvoroshenko, M. A. Ahmad, S. K. Mustafa, V. Lyashenko, and A. R. Alharbi, Modification of Models Intensive Development Ontologies by Fuzzy Logic, Int. J. Emerg. Trends Eng. Res. 8(3) (2020) 939-944.
[18] Y. I. Daradkeh, and I. Tvoroshenko, Technologies for Making Reliable Decisions on a Variety of Effective Factors using Fuzzy Logic, Int. J. Adv. Comput. Sci. Appl. 11(5) (2020) 43-50.
[19] J. Wu, W. Lin, G. Shi, Y. Zhang, W. Dong, and Z. Chen, Visual Orientation Selectivity Based Structure Description, IEEE Trans. Image Process. 24(11) (2015) 4602-4613.
[20] M. Ghahremani, Y. Liu, and B. Tiddeman, FFD: Fast Feature Detector, IEEE Trans. Image Process. 30 (2021) 1153-1168.
[21] Q. Bai, S. Li, J. Yang, Q. Song, Z. Li, and X. Zhang, Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey, IEEE Access. 8 (2020) 181855-181879.
[22] Y. Yang, X. Wang, B. Sun, and Q. Zhao, Channel Expansion Convolutional Network for Image Classification, IEEE Access. 8 (2020) 178414-178424.
[23] J. C. Chavali, D. Abraham Chandy. Detection of Aircraft Technical System Failuresor Malfunctionsby Using Image / Video Processing of Cockpit Panels, Int. J. Eng. Trends Technol. 69(7) (2021) 20-28.
[24] V. A. Gorokhovatsky, Image Likelihood Measures of the Basis of the Set of Conformities, Telecommun. Radio Eng. 68(9) (2009) 763-778.
[25] V. Gorokhovatskyi, and I. Tvoroshenko, Image Classification Based on the Kohonen Network and the Data Space Modification, in CEUR Workshop Proc.: Comput. Model. Intell. Syst. (CMIS-2020), 2608 (2020) 1013-1026.
[26] Y. I. Daradkeh, I. Tvoroshenko, V. Gorokhovatskyi, L. A. Latiff, and N. Ahmad, Development of Effective Methods for Structural Image Recognition Using the Principles of Data Granulation and Apparatus of Fuzzy Logic, IEEE Access. 9 (2021) 13417-13428.
[27] M. A. Ahmad, I. Tvoroshenko, J. H. Baker, L. Kochura, and V. Lyashenko, Interactive Geoinformation Three-Dimensional Model of a Landscape Park Using Geoinformatics Tools, Int. J. Adv. Sci., Eng. Inf. Technol. 10(5) (2020) 2005-2013.
[28] I. S. Tvoroshenko, and V. ?. Gorokhovatsky, Effective tuning of membership function parameters in fuzzy systems based on multivalued interval logic, Telecommun. Radio Eng. 79(2) (2020) 149- 163.
[29] Y. I. Daradkeh, V. Gorokhovatskyi, I. Tvoroshenko, S. Gadetska, and M. Al-Dhaifallah, Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components, IEEE Access. 9 (2021) 92964- 92973.
[30] R. Szeliski, Computer Vision: Algorithms and Applications. London, Great Britain: Springer-Verlag. (2010) 655-718.
[31] M. A. Ahmad, I. Tvoroshenko, J. H. Baker, and V. Lyashenko, Computational Complexity of the Accessory Function Setting Mechanism in Fuzzy Intellectual Systems, Int. J. Adv. Trends Comput. Sci. Eng. 8(5) (2019) 2370-2377.
[32] N. A. Shnain, Z. M. Hussain, and S. F. Lu, A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition, Appl. Sci. 7(8) (2017) 786.
[33] Pokemon image library, Accessed (2021) [Online]. Available: https://www.pokemon.com/ru/pokedex/.
[34] IntelliJ IDEA: The Java IDE for Professional Developers by JetBrains, Accessed (2021) [Online]. Available: https://www.jetbrains.com/idea.
[35] OpenCV Open Source Computer Vision, Accessed (2021) [Online]. Available: https://docs.opencv.org/master/ index.html.
[36] V. ?. Gorokhovatskyi, I. S. Tvoroshenko, and O. ?. Peredrii, Image classification method modification based on model of logic processing of bit description weights vector, Telecommun. Radio Eng. 79(1) (2020) 59-69.
[37] J. Yang, W. Zhang, X. Li, T. Zhou, and B. Ou, Full Reference Image Quality Assessment by Considering Intra-Block Structure and Inter- Block Texture, IEEE Access. 8 (2020) 179702-179715.
[38] S. Kim, I. S. Kweon, Biologically Motivated Perceptual Feature: Generalized Robust Invariant Feature, in Proc. Asian Conf. Comput. Vis. (ACCV), 3852 (2006) 305-314.
[39] J. H. Baker, V. Lyashenko, S. Sotnik, F. Laariedh, S. K. Mustafa, M. A. Ahmad, Some Interesting Features of Semantic Model in Robotic Science, International Journal of Engineering Trends and Technology, Vol. 69 (7) (2021) 38-44.
[40] M. Ayaz Ahmad, I. Tvoroshenko, J. H. Baker, V. Lyashenko, Modeling the Structure of Intellectual Means of Decision-Making Using a System-Oriented NFO Approach. International Journal of Emerging Trends in Engineering Research. 7(11) (2019) 460-465.
[41] Mbunwe Muncho J., Ezema Ejiofor E., Ngwu Anene A., C V Anghel Drugarin, M. Rehan Ajmal, M Ayaz Ahmad, Characterization of three phase solid state VAR compensation scheme in three phase pulse width modulation voltage source inverter, Journal of Physics: Conference Series. 1781 (2021) 012034
[42] Mykhailo Kopot, M. Ayaz Ahmad, Vyacheslav Lyashenko, Syed Khalid Mustafa, Prospects for Creating Sub-Millimeter Magnetrons, International Journal of Advanced Trends in Computer Science and Engineering, 9(4) (2020) 6184-6188
[43] Mircea Resteiu, Remus Dobra, Dragos Pasculescu, M. Ayaz Ahmad, Quality Engineering Tools Focused on Designing Remote Temperature Measurements for Inaccessible Locations by Using Light Components Parameterization of the Heated Materials, IOP Conference Series Materials Science and Engineering, 133 (2016) 012059.
[44] Mir Hashim Rasool, M. Ayaz Ahmad, Shafiq Ahmad, Signature of intermittency during emission of target associated particles in heavy ion collisions at SPS energies, Journal of Mathematical and Computational Science, 11(2) (2021) 2263-2279.
[45] M. Ayaz Ahmad, Jalal Hasan Baker, Mir Hashim Rasool, Shafiq Ahmad, R. Dobra, D. Pasculescu & Charles Roberto Telles, Fluctuations in produced charged particle multiplicities in relativistic nuclear collisions for simulated events, IOP Conf. Series: Journal of Physics: Conf. Series. 1258 (2019) 012010 (1-9).
[46] Igor Nevliudov, Vladyslav Yevsieiev, Jalal Hasan Baker, M. Ayaz Ahmad, Vyacheslav Lyashenko, Development of a cyber design modeling declarative Language for cyber physical production systems, Journal of Mathematical and Computational Science, Vol. 11(1), (2021), 520-542
[47] Muncho J. Mbunwe, M. Ayaz Ahmad, Syed Khalid Mustafa, An effective energy saving design strategy to maximize the use of electricity, J. Math. Comput. Sci., 10(5) (2020) 1808-1833

Keywords
Computer Vision, Descriptor, Image Classification, Keypoint, ORB Detector.