Image Recognition Using Machine Learning with the Aid of MLR

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Author(s)

Meherunnesa Tania 1,* Diba Afroze 1 Jesmin Akhter 2 Abu Sayed Md. Mostafizur Rahaman 1 Md. Imdadul Islam 1

1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

2. Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2021.06.02

Received: 10 Aug. 2021 / Revised: 2 Sep. 2021 / Accepted: 22 Sep. 2021 / Published: 8 Dec. 2021

Index Terms

Logistic regression, Eigen decomposition, objective function, scatterplot and entropy based combined algorithm

Abstract

In this paper, we use three machine learning techniques: Linear Discriminant Analysis (LDA) along different Eigen vectors of an image, Fuzzy Inference System (FIS) and Fuzzy c-mean clustering (FCM) to recognize objects and human face. Again, Fuzzy c-mean clustering is combined with multiple linear regression (MLR) to reduce the four-dimensional variable into two dimensional variables to get the influence of all variables on the scatterplot. To keep the outlier within narrow range, the MLR is again applied in logistic regression. Individual method is found suitable for particular type of object recognition but does not reveal standard range of recognition for all types of objects. For example, LDA along Eigen vector provides high accuracy of detection for human face recognition but very poor performance is found against discrete objects like chair, butterfly etc. The FCM and FIS are found to provide moderate result in all kinds of object detection but combination of three methods of the paper provide expected result with low process time compared to deep leaning neural network.  

Cite This Paper

Meherunnesa Tania, Diba Afroze, Jesmin Akhter, Abu Sayed Md. Mostafizur Rahaman, Md. Imdadul Islam, " Image Recognition Using Machine Learning with the Aid of MLR", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.13, No.6, pp. 12-22, 2021. DOI: 10.5815/ijigsp.2021.06.02

Reference

[1]P. Borisagar, S. Jani, Y. Agrawal and R. Parekh, ‘An Efficient and Compact Review of Face Recognition Techniques,’ 2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 2020, pp. 1-5, 22-23 Feb. 2020, Bhopal, India

[2]A. Dechemi and N. Achour, ‘An Approach of Data Fusion for FuzzyART Based Visual Recognition,’ 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), 2019, pp. 86-90, 16-17 April 2019, Istanbul, Turkey

[3]D. Szostak, K. Walkowiak and A. W┼éodarczyk, ‘Short-Term Traffic Forecasting in Optical Network using Linear Discriminant Analysis Machine Learning Classifier,’ 2020 22nd International Conference on Transparent Optical Networks (ICTON), 2020, pp. 1-4, 22 September 2020, Bari, Italy

[4]G. B. G. Pereira, L. P. Fernandes, J. M. R. d. S. Neto, H. D. d. M. Braz and L. da Silva Sauer, ‘A comparative study of linear discriminant analysis and an artificial neural network performance in breast cancer diagnosis,’ 2020 IEEE ANDESCON, 2020, pp. 1-6, December 2020, Quito, Ecuador

[5]Tharwat Alaa, Gaber Tarek, Ibrahim Abdelhameed and Hassanien Aboul Ella, ‘Linear discriminant analysis: A detailed tutorial,’ Ai Communications, vol.30, no.2, pp.169-190, 2017

[6]Y. Wang, K. Zhang and Z. Sun, ‘A Novel Deep-learning Pipeline for Light Field Image Based Material Recognition,’ 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 2422-2429, 10-15 Jan. 2021, Milan, Italy 

[7]M. K. Halidu, P. Bagheri-Zadeh, A. Sheikh-Akbari and R. Behringer, ‘PCA in the context of Face Recognition with the Image Enlargement Techniques,’ 2019 8th Mediterranean Conference on Embedded Computing (MECO), 2019, pp. 1-5, 0-14 June 2019, Budva, Montenegro

[8]Rahil Bensaid, Maymouna Ben Said and Hatem Boujemaa, ‘Fuzzy C-Means based Clustering Algorithm in WSNs for IoT Applications,’ 2020 International Wireless Communications and Mobile Computing (IWCMC), pp.126-130, 15-19 June 2020, Limassol, Cyprus 

[9]Md Abul Kalam Azad, Anup Majumder, Jugal Krishna Das, Md Imdadul Islam, ‘Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules,’ Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 1140-1150, February 2021 

[10]E. Bayhan, Z. Ozkan, M. Namdar and A. Basgumus, ‘Deep Learning Based Object Detection and Recognition of Unmanned Aerial Vehicles,’ 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2021, pp. 1-5, 11-13 June 2021, Ankara, Turkey

[11]S. Hayat, S. Kun, Z. Tengtao, Y. Yu, T. Tu and Y. Du, ‘A Deep Learning Framework Using Convolutional Neural Network for Multi-Class Object Recognition,’ 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018, pp. 194-198, 27-29 June 2018, Chongqing, China

[12]L. Yan, Y. Wang, T. Song and Z. Yin, ‘An incremental intelligent object recognition system based on deep learning,’ 2017 Chinese Automation Congress (CAC), 2017, pp. 7135-7138, 20-22 Oct. 2017, Jinan, China

[13]Tharwat Alaa, Gaber Tarek, Ibrahim Abdelhameed and Hassanien Aboul Ella, ‘Linear discriminant analysis: A detailed tutorial,’ Ai Communications, vol.30, no.2, pp.169-190, 2017

[14]X. Liu, Z. Tang, H. Huang, T. Zhang and B. Yang, ‘Multiple Learning for Regression in Big Data,’ 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), 2019, pp. 587-594, 16-19 Dec. 2019, Boca Raton, FL, USA

[15]Pramit Dutta and Nafisa Anjum, ‘Optimization of Temperature and Relative Humidity in an Automatic Egg Incubator Using Mamdani Fuzzy Inference System,’ 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp.12-16, 5-7 Jan. 2021, DHAKA, Bangladesh

[16]J. Sun, Y. Dai and K. Zhao, ‘DBFCM: a Density-Based Fuzzy C-Means with Self-Regulated Fuzzy Clustering Parameters,’ 2020 39th Chinese Control Conference (CCC), 2020, pp. 2233-2238, 27-29 July 2020, Shenyang, China

[17]Md Abul Kalam Azad, Anup Majumder, Muhammad R. A. Khandaker, Jugal K. Das, Md. Imdadul Islam, ‘Primary User Aided Cognitive Radio Network with Optimum Location of Relay,’ International Journal of Scientific & Technology Research, Volume 9, Issue 09, pp.217-221, September 2020

[18]J. Yu and X. Lu, ‘Wafer Map Defect Detection and Recognition Using Joint Local and Nonlocal Linear Discriminant Analysis,’ in IEEE Transactions on Semiconductor Manufacturing, vol. 29, no. 1, pp. 33-43, Feb. 2016

[19]N. M. Tahir, Adam N. Ausat, Usman I. Bature, Kamal A. Abubakar and Ibrahim Gambo, "Off-line Handwritten Signature Verification System: Artificial Neural Network Approach", I.J. Intelligent Systems and Applications, vol.13, no.1, pp.45-57, 2021.

[20]Anozie Onyezewe, Armand F. Kana, Fatimah B. Abdullahi and Aminu O. Abdulsalami, "An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing", I.J. Intelligent Systems and Applications, pp.34-44, vol.13, no.1, pp.45-57, 2021.