image-comparison

Several questions have been asked about the SIFT algorithm , but they all seem focussed on a simple comparison between two images. Instead of determining how similar two images are, would it be practical to use SIFT to find the closest matching image out of a collection of thousands of images? In other words, is SIFT scalable? For example, would it be practical to use SIFT to generate keypoints for a batch of images, store the keypoints in a database, and then find the ones that have the shortest Euclidean distance to the keypoints generated for a "query" image? When calculating the Euclidean

Is there a quick and easy way to do such comparison? I've found few image compare questions from stackoverflow but none of those actually proved answer for this question. I have images files in my filesystem and a script that fetches images from urls. I want to check if the image in url is already the same that is on disk. Normally I would load the image in disk and url to a PIL object and use following function I found: def equal(im1, im2): return ImageChops.difference(im1, im2).getbbox() is None but this doesn't work if you have a image saved to disk with PIL as it gets compressed even if

[EDIT] I have devised some code for image comparison. The matching part is still a bit flawed and I would love some assitance. The project can be found at - GitHub . I have these two images Img1 and Img2 : When I use the following command in openCV Mat img1 = Highgui.imread("mnt/sdcard/IMG-20121228.jpg"); Mat img2 = Highgui.imread("mnt/sdcard/IMG-20121228-1.jpg"); try{ double l2_norm = Core.norm( img1, img2 ); tv.setText(l2_norm+""); } catch(Exception e) { //image is not a duplicate } I get a double value for l2_norm. This double value varies for duplicate image pairs. But if the images are

I have used the SIFT implementation of Andrea Vedaldi , to calculate the sift descriptors of two similar images (the second image is actually a zoomed in picture of the same object from a different angle). Now I am not able to figure out how to compare the descriptors to tell how similar the images are ? I know that this question is not answerable unless you have actually played with these sort of things before, but I thought that somebody who has done this before might know this , so I posted the question. the little I did to generate the descriptors: >> i=imread('p1.jpg'); >> j=imread('p2

I need an algorithm that can determine whether two images are 'similar' and recognizes similar patterns of color, brightness, shape etc.. I might need some pointers as to what parameters the human brain uses to 'categorize' images. .. I have looked at hausdorff based matching but that seems mainly for matching transformed objects and patterns of shape. I have done something similar, by decomposing images into signatures using wavelet transform . My approach was to pick the most significant n coefficients from each transformed channel, and recording their location. This was done by sorting the