cbir

Project: Content Based Image Retrieval - Semi-supervised (manual tagging is done on images while training) Description I have 1000000 images in the database. The training is manual (supervised) - title and tags are provided for each image. Example: coke.jpg Title : Coke Tags : Coke, Can Using the images and tags, I have to train the system. After training, when I give a new image (already in database/ completely new) the system should output the possible tags the image may belong to and display few images belonging to each tag. The system may also say no match found. Questions: 1) What is mean

There's a lot of different algorithms for computing the similarity between two images, but I can't find anything on how you would store this information in a database such that you can find similar images quickly. By "similar" I mean exact duplicates that have been rotated (90 degree increments), color-adjusted, and/or re-saved (lossy jpeg compression). I'm trying to come up with a " fingerprint " of the images such that I can look them up quickly. The best I've come up with so far is to generate a grayscale histogram . With 16 bins and 256 shades of gray, I can easily create a 16-byte

I'd like to get the most common color from an image. I use Java and I want to have the most predominant color. Are there any cbir java library to make this? Thanks How accurate do you want this to be? You can use Bozhos's approach and loop over the entire image but this could be slow for large images. There are 16777216 possible RGB values and keeping counters for them in a Map is not very efficient. An alternative is to resample the image using getScaledInstance to scale it down to a smaller version e.g. a 1x1 image and then use getRGB to get the colour of that pixel. You can experiment with