Image Color Segmentation With Kdtree Library For Car Color Identity Classification

Authors

  • Joni Computer Science Dept, STMIK TIME, Medan, Indonesia
  • Erwin Accounting Dept, STIE Mikroskil, Medan, Indonesia

DOI:

https://doi.org/10.26438/ijcse/v9i6.912

Keywords:

Color Segmentation, Color Identification, KDTree Library, Car Color

Abstract

Joni and Erwin, Abstract, Artificial Intelligence (AI) has been widely used in analyzing objects, such as text, image, etc. There are many things that can be analyzed from images for the needs of identifying and classifying objects into certain types. One of the identifiable data is color. To identify the main color of an object, a vehicle image (car) requires a very complex analysis process. In this study, the identification process was carried out using an image center area analysis approach. This is based on the perception that the main color is in the middle of the object area. All color pixels in the analyzed area are converted to color names using the KDTree library. The segmentation process will produce several groups of color values. From the color matrix that has been through the segmentation process, the color identity of the object is obtained, which is determined by the mode value of the color matrix

References

[1] Astha Pathak, Avinash Dhole, "Image classification Method in detecting Lungs Cancer using CT images: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.37-42, 2021.

[2] Yash Baid, Avinash Dhole, "Food Image Classification Using Machine Learning Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.31-36, 2021.

[3] M. Lalitha, M. Kiruthiga, and C. Loganathan, “A survey on image segmentation through clustering algorithm,” International Journal of Science and Research, vol. 2, no. 2, pp. 348–358, 2013.

[4] N. Sharma, M. Mishra, and M. Shrivastava, “Colour image segmentaion techniques and issues: an approach,” International Journal of Scientific & Technology Research, vol. 1, no. 4, pp. 9–12, 2012.

[5] L. Busin, N. Vandenbroucke, and L. Macaire, “Color spaces and image segmentation,” Advances in Imaging and Electron Physics, vol. 151, pp. 65–168, 2008.

[6] R. Rulaningtyas, A. B. Suksmono, T. L. R. Mengko, G. A. P. Saptawati, “Segmentasi Citra Berwarna dengan Menggunakan Metode Clustering Berbasis Patch untuk Identifikasi Mycobacterium Tuberculosis” (Color Image Segmentation Using Patch-Based Clustering Method for Identification of Mycobacterium Tuberculosis), Jurnal Biosains Pascasarjana, Vol. 17, No. 1, pp. 19-25, August 2015.

[7] M. G. Alfianto, R. N. Whidhiasih, Maimunah, “Identifikasi Beras Berdasarkan Warna Menggunakan Adaptive Neuro Fuzzy Inference System” (Rice Identification Based on Color Using Adaptive Neuro Fuzzy Inference System), Jurnal Penelitian Ilmu Komputer, Sistem Embedded & Logic, Vol. 5, No. 2, pp. 51-59, 2017.

Downloads

Published

2021-06-30
CITATION
DOI: 10.26438/ijcse/v9i6.912
Published: 2021-06-30

How to Cite

[1]
Joni and Erwin, “Image Color Segmentation With Kdtree Library For Car Color Identity Classification”, Int. J. Comp. Sci. Eng., vol. 9, no. 6, pp. 9–12, Jun. 2021.

Issue

Vol. 9 No. 6 (2021): IJCSE June Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.