Issue

Vol. 5, No. 2, May 2020

Image Retrieval Based on Texton Frequency-Inverse Image Frequency

https://doi.org/10.22219/kinetik.v5i2.1026
Yufis Azhar
Universitas Muhammadiyah Malang
Agus Eko Minarno
Universitas Muhammadiyah Malang
Yuda Munarko
Universitas Muhammadiyah Malang
Zaidah Ibrahim
Universiti Teknologi MARA, Shah Alam, Selangor

Corresponding Author(s) : Yufis Azhar

yufis@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 2, May 2020

Abstract

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method.

Keywords

Image Retrieval Feature Weighting Texton Co-occurrence
Azhar, Y., Minarno, A. E., Munarko, Y., & Ibrahim, Z. (2020). Image Retrieval Based on Texton Frequency-Inverse Image Frequency. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(2), 87-94. https://doi.org/10.22219/kinetik.v5i2.1026
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Author Biographies

Yufis Azhar, Universitas Muhammadiyah Malang

Google Scholar Profil:

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Zaidah Ibrahim, Universiti Teknologi MARA, Shah Alam, Selangor

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