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Image Color Segmentation With Kdtree Library For Car Color Identity Classification

Joni 1 , Erwin 2

Section:Research Paper, Product Type: Journal Paper
Volume-9 , Issue-6 , Page no. 9-12, Jun-2021

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v9i6.912

Online published on Jun 30, 2021

Copyright © Joni, Erwin . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Joni, Erwin, “Image Color Segmentation With Kdtree Library For Car Color Identity Classification,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.9-12, 2021.

MLA Style Citation: Joni, Erwin "Image Color Segmentation With Kdtree Library For Car Color Identity Classification." International Journal of Computer Sciences and Engineering 9.6 (2021): 9-12.

APA Style Citation: Joni, Erwin, (2021). Image Color Segmentation With Kdtree Library For Car Color Identity Classification. International Journal of Computer Sciences and Engineering, 9(6), 9-12.

BibTex Style Citation:
@article{_2021,
author = {Joni, Erwin},
title = {Image Color Segmentation With Kdtree Library For Car Color Identity Classification},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2021},
volume = {9},
Issue = {6},
month = {6},
year = {2021},
issn = {2347-2693},
pages = {9-12},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5339},
doi = {https://doi.org/10.26438/ijcse/v9i6.912}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v9i6.912}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5339
TI - Image Color Segmentation With Kdtree Library For Car Color Identity Classification
T2 - International Journal of Computer Sciences and Engineering
AU - Joni, Erwin
PY - 2021
DA - 2021/06/30
PB - IJCSE, Indore, INDIA
SP - 9-12
IS - 6
VL - 9
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

Color Segmentation, Color Identification, KDTree Library, Car Color

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.