| Purpose: The algorithm is to retrieve images similar to the input image. |
| Input: An RGB image, number of retrieved images n. |
| Output: n images similar to the input image. |
| Method: |
| Step 1 : The input image is a color image in RGB color space. |
| Step 2 : Apply the Ranklet Transform for each image layer (R, G, and B). The output images will be in three orientations (vertical, |
| horizontal, and diagonal). |
| Step 3 : For each ranklet image (vertical, horizontal, and diagonal) in a specified layer, calculate the color moments (8), |
| (9), and (10). |
| Step 4 : Construct the feature vector that will represent the image containing 27 numerical values. |
| Step 5 : Cluster the images in the database using k-means algorithm (Algorithm 1) into different categories. |
| Step 6 : Calculate the distance between the input image and the centroid of each cluster using Euclidian Distance, and find the |
| smallest distance. |
| Step 7 : Calculate the distance between the input image and the images in the cluster that has the smallest distance with the |
| input image. |
| Step 8 : Retrieve the first n images that is similar to the input image. |