A typical image database query would be to find images in the database that are similar to a given image. The given image could be an isolated segment that contains, say, a pattern of interest, and the query is to locate other images that contain that same pattern. There are two main techniques for this type of search. The first approach uses a distance function to compare the given image with the stored images and their segments. If the distance value returned is small, the probability of a match is high. Indexes can be created to group stored images that are close in the distance metric so as to limit the search space. The second approach, called the transformation approach, measures image similarity by having a small number of transformations that can change one image$$s cells to match the other image. Transformations include rotations, translations, and scaling. Although the transformation approach is more general, it is also more time$-$consuming and difficult. Please write an article distance function and transformation approach for image database query