Image Similarity Searching Use Multi Part Cutting And Grayscale Color Histogram

Sofyan Pariyasto, Kusrini Kusrini, Hanif Al Fatta

Abstract


The use of information technology in everyday life continues to increase so rapidly. This is inseparable from the role of researchers, especially in the field of information technology. Information technology has become a necessity so that it is widely used in the fields of education, trade, livestock and even to the agricultural sector. One of the obstacles that is needed is to check all activities involving information systems, especially when there is data in the form of images. Problems that arise are usually needed by humans to check and sort items that have been carried out by humans. This is the background of this research to help reduce activities involving humans. The process of finding the similarity of images in computer vision can be used in several fields such as education, retail, and other fields. In the field of computer vision education can be utilized for the automatic absence process through face recognition, in terms of retailing, it can be used for sorting through object detection. The process of finding similarities between images that are queries and dataset images will be the subject of research, and the process of calculating similarities between queries and datasets will be discussed step by step. The method used in the search process is by calculating the shortest distance between query images and dataset. The steps taken are the extraction feature and then RGB to gray color conversion. The next stage is to cut the image into four parts which will then be calculated the distance of the ecludian. The final part will calculate the performance of the algorithm using the matrix confusion method, so that the test results are in the form of error rates, precision, and accuracy. The trial process uses 30 data using 1000 datasets. In the test results obtained information in the form of recall of 1, 0.66 accuracy and 0.66precision.

Keywords: Image Similarity, Histogram Image Grayscale, Ecludiance Distance.


References


L. Liu, M. Yu, and L. Shao, “Image Similarity Search,” vol. 46, no. 11, pp. 1–11, 2016.

A. Baita, B. S. W, and A. Sunyoto, “Logo Retrieval Berdasarkan Ekstraksi Multifitur,” Magistra, no. 98, pp. 53–59, 2016.

C. Digital, “Citra Digital,Warna, CBIR, RGB, HSV, Sistem, Histogram. 1.,” vol. 19, no. 1, 2016.

F. Flaurensia, T. Rismawan, R. Hidayati, and J. S. Komputer, “PENGENALAN MOTIF BATIK INDONESIA MENGGUNAKAN DETEKSI TEPI CANNY DAN TEMPLATE MATCHING,” vol. 4, no. 2.

M. Pada and C. A.- Qur, “SISTEM PENDETEKSI POLA LAFADZ ALLAH DAN.”

J. Mukherjee, I. K. Maitra, K. N. Dey, S. K. Bandyopadhyay, D. Bhattacharyya, and T. H. Kim, “Grayscale conversion of histopathological slide images as a preprocessing step for image segmentation,” Int. J. Softw. Eng. its Appl., vol. 10, no. 1, pp. 15–26, 2016.

J. Li and B. Lu, “An adaptive image Euclidean distance,” vol. 42, pp. 349–357, 2009.

S. P. M. Choi, S. S. Lam, K. C. Li, and B. T. M. Wong, “Learning Analytics at Low Cost : At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions,” vol. 21, pp. 273–290, 2018.

M. A. Nugroho and H. A. Santoso, “KLASIFIKASI DOKUMEN KOMENTAR PADA SITUS YOUTUBE MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR ( K-NN ),” no. 5.

X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf. Sci. (Ny)., 2016.

A. M. Abdel-zaher and A. M. Eldeib, “PT US CR,” Expert Syst. Appl., 2015.

E. R. Ariyanto, “Implementasi Deteksi Citra Pornografi Berbasis Model Warna YCbCr dengan Metode Perbaikan C4.5 dan Shape Descriptor Untuk Filter Upload Foto di Media Sosial,” pp. 1–6.




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DOI: 10.30595/techno.v20i1.3817

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ISSN: 2579-9096