Searching Similarity Digital Image Using Color Histogram
In the era of globalization and modernization, as now, information technology is widely used in the fields of education, trade, animal husbandry, agriculture and even to the legal sector. One branch of science in the field of information technology that is growing rapidly is computer vision. One of the important roles of computer vision in everyday life is the use of computer vision. This can be applied in terms of face recognition, object detection, and can be applied to group images based on the order of similarity of the image, the ability of computer vision is applied to facilitate human work in selecting from several images to find the most similar images. In this study described the process of finding the similarity of an image with other images through several stages of research flow, the method used is to use RGB values that have been converted to grayscale, then the eucludian distance distance is calculated to determine the value of proximity of an image while calculating performance accuracy algorithm using confusion matrix. The search trial process resulted in an accuracy rate of 0.42, precision of 0.42 and recall 1 of 1000 datasets and 30 random data were taken. Found images that differ in color and shape but when converted into histograms the data has a fairly high similarity to the query. The disadvantage of this research is that images that have histograms similar to queries are displayed as similar images even though the reality is that images are very different from colors and shapes.
Keywords: computer vision, similiarity, eucludian distance, grayscale, histogram
H. Province and H. Province, “万勋 1 李云丰 2 ，彭敏放 1* (1,” vol. 44, no. November, pp. 103–109, 2018.
HISTOGRAM-BASED SEARCH : A COMPARATIVE STUDY Mikhail Sizintsev , Konstantinos G . Derpanis Department of Computer Science and Engineering Toronto , ON , Canada Faculty of Business and Information Technology University of Ontario Institute of Technology Os,” Proc. 21st IEEE Conf. Comput. Vis. Pattern Recognit. - CVPR ’08, 2008.
A. Baita, B. S. W, and A. Sunyoto, “Logo Retrieval Berdasarkan Ekstraksi Multifitur,” Magistra, no. 98, pp. 53–59, 2016.
C. Kavitha, D. Rao, and D. Govardhan, “Image retrieval based on color and texture features of the image sub-blocks,” Int. J. …, vol. 15, no. 7, pp. 33–37, 2011.
N. C. Santi, “Mengubah Citra Berwarna Menjadi Gray¬Scale dan Citra biner. Jurnal Teknologi Informasi DINAMIK,” J. Teknol. Inf. Din., vol. 16, no. 1, pp. 14–19, 2011.
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. Sangoh, “Histogram-Based Color Image Retrieval,” Psych221/EE362 Proj. Report, Stanford Univ., pp. 1–21, 2008.
S. Kusumaningtyas and R. A. Asmara, “Identifikasi Kematangan Buah Tomat Berdasarkan Warna Menggunakan Metode Jaringan Syaraf Tiruan (Jst),” J. Inform. Polinema, vol. 2, no. 2, pp. 72–75, 2016.
A. H. Rangkuti, N. Hakiem, R. B. Bahaweres, A. Harjoko, and A. E. Putro, “Analysis of image similarity with CBIR concept using wavelet transform and threshold algorithm,” IEEE Symp. Comput. Informatics, Isc. 2013, no. June 2017, pp. 122–127, 2013.
C. Iswahyudi, “Prototype Aplikasi Untuk Mengukur Kematangan Buah Apel,” J. Teknol., vol. 3, pp. 107–112, 2010.
J. Li and B. L. Lu, “An adaptive image Euclidean distance,” Pattern Recognit., vol. 42, no. 3, pp. 349–357, 2009.
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.
This work is licensed under a Creative Commons Attribution 4.0 International License.