Seleksi Citra Berdasarkan Ciri dengan Algoritma Threshold Mengunakan Fuzzy Kurva S dan Fungsi Min


  • A. Haris Rangkuti Bina Nusantara University



adaptive histogram, invariant moment, S-curve, threshold algorithm, grade value, CBIR, sigmoid, Euclid, min, fuzzy


Image retrieval process of fruits and flowers with CBIR concept was represented by the colors and shapes using adaptive histogram method for color, and invariant moment for shape. To measure the similarity between the query image and the basis data image Euclidean distance function was used, where the result is f(x). Calculations for f (y) through the process of ‘fuzzy-ing’-S curve, where the value of f(x) guides the sigmoid function. The value f(y) on each image than the threshold value based image query. Basically, the algorithm displays the image based on Threshold features, by comparing the threshold value with the value f(y). A high grade value (approaching 1) indicates that the feature of the sample (query) image is similar to the basis data image, and vice versa. The process was continued by comparing the value grades of the image representation of color and form using min operator in fuzzy logic, so that it only displayed several images that have some resemblances in accordance with the original image. The advantage of threshold algorithm and the fuzzy function - compared to other methods – lies in the simplicity method in the image retrieval, so that the performance of CBIR becomes more reliable and effective.


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Fagin, R. (1998). Fuzzy queries in Multimedia basis data System. Proceeding of ACM Sysmposium on Principle of Basis Data System, Seatle.

Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE PAMI, 22(12), 1349-1380.

Xiaoling, W., Kanglin, X. (2005). Application of the fuzzy logic in content-based image retrieval.JCS&T, 5(1), 19 – 24.






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