K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

Author

Ahmad Wali Satria Bahari Johan, Fitri Utaminingrum, Agung Setia Budi

Abstract

This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair

Full Text:

PDF

References


D. D. Bhavani, A. Vasavi, and P. T. Keshava.: Machine Learning : A Critical Review of Classification Techniques. pp. 22–28, 2016.

P. Mulak and N. Talhar.: Analysis of Distance Measures Using K-Nearest Neighbor Algorithm on KDD Dataset. vol. 4, no. 7, pp. 2101–2104, 2015.

A. Rohman.: k-nearest neighbor (k-nn) algorithm model for student graduation prediction. 2012.

M. Akhil, B. L. Deekshatulu, and P. Chandra.: Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm. Procedia Technol., vol. 10, pp. 85–94, 2013.

Rai Munoz.: Depth-Aware Indoor Staircase Detection And Recognition For The Visually Impaired. Dept . of Electrical Engineering The City College of New York , CUNY New York, NY 10031.

A.Wali Satria Bahari Johan and Fitri Utaminingrum.: Stairs Descent Identification for Smart Wheelchair by Using GLCM and Learning Vector Quantization. pp. 64–68, 2019.

S. Macˆ and J. Kelner.: A comparative study of grayscale conversion techniques applied to SIFT descriptors. vol. 6, no. 2, pp. 30–36, 2015.

S. J. A. Sarosa.: Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine. 2018 Int. Conf. Sustain. Inf. Eng. Technol., pp. 54–59, 2018.

C. Dewi and S. Sundari.: Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network. vol. 4, no. 1, pp. 1–14, 2019.

M. A. Ben Atitallah, R. Kachouri, M. Kammoun and H. Mnif.: An efficient implementation of GLCM algorithm in FPGA. 2018 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), Hamammet, Tunisia, 2018, pp. 147-152.

M. Hall-beyer.: Glcm Texture : A Tutorial. no. March, 2017.

M. S. Sarma, Y. Srinivas, M. Abhiram, L. Ullala, M. S. Prasanthi and J. R. Rao.: Insider Threat Detection with Face Recognition and KNN User Classification. 2017 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Bangalore, 2017, pp. 39-44.

S. S. Tabrizi and N. Cavus.: A Hybrid KNN-SVM Model for Iranian License Plate Recognition. Procedia Comput. Sci., vol. 102, no. September, pp. 588–594, 2016.

P. Sonar, U. Bhosle, and C. Choudhury.: Mammography classification using modified hybrid SVM-KNN. Proc. IEEE Int. Conf. Signal Process. Commun. ICSPC 2017, vol. 2018–Janua, no. July, pp. 305–311, 2018.

A. Ahirwar.: Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI. no. April, pp. 44–53, 2013.




DOI: http://dx.doi.org/10.25126/jitecs.202051144