If you are not familiar with K-Means you can find more articles/projects about it on my blog: The bigger the K, the more regions should appear.
In order for K-Means to segment the image into regions, we first assign the number of K (number of regions) for the image. Select number of regions for Image Search If you wish to search for a RGB image, you can grayscale it, and then continue with the same procedure. In the video presentation above I have chosen the famous “Cameraman” image.Ĭameraman image is heavily used image in the Image Processing fieldįor this project we will be using grayscale images. Since we are about to do an image search, we need to select an image. Doing an image searching based on the context of the image will yield even better results. In the next part we will look at the context of the image rather then just measuring region similarity. Like I mentioned already, for now we will be using a very basic concept to calculate the similarity, but this is only part one.
Google will find the most similar/exact match to your image search query. Google has implemented their own way of Reverse Image Search algorithm and you can check it our here.Ĭhoose an image, and drop it down or upload it to. The idea is to divide the image into regions, and base our similarity percentage on that. There are plenty of ways how you can calculate that percentage, but we will do it using K-Means and image segmentation. This project is all about comparing two images and measure their similarity. Find the most similar image based on the regions extracted and processed from both images.Run KMeans to segment that image into regions.Initialize number and color, of segments for that image.Select number of regions for Image Search.
0 kommentar(er)
