Menu
I am thinking about creating a database system for images where they are stored with compact signatures and then matched against a "query image" that could be a resized, cropped, brightened, rotated or a flipped version of the stored one. Note that I am not talking about image similarity algorithms but rather strictly about duplicate detection. This would make things a lot simpler. The system wouldn't care if two images have an elephant on them, it would only be important to detect if the two images are in fact the same image.
Histogram comparisons simply won't work for cropped query images. The only viable way to go I see is shape/edge detection. Images would first be somehow discretized, every pixel being converted to an 8-level grayscale for example. The discretized image will contain vast regions in the same colour which would help indicate shapes. These shapes then could be described with coefficients and their relative position could be remembered. Compact signatures would be produced out of that. This process will be carried out over each image being stored and over each query image when a comparison has to be performed. Does that sound like an efficient and realisable algorithm? To illustrate this idea:
removed dead ImageShack link
I know this is an immature research area, I have read Wikipedia on the subject and I would ask you to propose your ideas about such an algorithm.
873
3. Perceptual Hash. Good for finding exact duplicates or duplicates with tiny changes. A perceptual hash tries to overcome the limitations of file hashes.
The Google picture search is the most widely used image search engine due to its extensive database that contains billions of images uploaded over the web. It is best to use image search Google when your aim is to find identical pictures against your queried image.
The short answer is: “No.” In reality, Google does not penalize for duplicate content. It's in the way which Google displays results where some might think so. It's not like the worst types of penalties Google hands out for creating poor content. Duplicates are simply a victim of being ignored by the search engine.
SURF should do its job.
http://en.wikipedia.org/wiki/SURF
It is fast an robust, it is invariant on rotations and scaling and also on blure and contrast/lightning (but not so strongly).
There is example of automatic panorama stitching.
Check article on SIFT first
http://en.wikipedia.org/wiki/Scale-invariant_feature_transform
60
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
