Some limitations that I probably should have mentioned.steemCreated with Sketch.

in #photomag6 years ago (edited)

Hey everyone,

So, having thought about my introductory post, I realised that I missed some things that I thought were obvious, but weren't.

Of course, I have limited voting power and have to keep it above 80% to maintain a level of efficiency. For this reason, you may get a lower percentage vote at times. I will also judge percentage upvote based on a couple of things...

  • Originality - if a post is completely original to @photomag and hasn't been posted elsewhere before then the percentage upvote will be higher. (that is either on the blockchain, or on other portfolios)
  • Quality - obviously I am not going to upvote a million shitposts. You will be rewarded for the effort you put in and the quality of your work.

I will probably also limit the number of up-votes I give to a single poster in a week or else I'm going to be inundated with people dropping single image posts from their existing portfolios.

This is all going to be fluid for a while until the whole thing (hopefully) gets more traction so please bear with me. I'll upvote what I can at the level I think it deserves.

Also, I'll only be upvoting English language posts, as that's the only language I can speak.

Cheers

@photomag.
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Currently im working on some tineye equivalent for steemit for searching for duplicate and in the future also similar images. Would you be interested? Im pretty New to programming (2-3 years as a Hobby)

Will it present better results than Google image search?
What exactly are you trying to achieve?

Well it was planned to use to check if a picture was already uploaded/used on steemit or not.

It could be more exact than Google for certain cases because the aproach is different. I calculate a imagehash(p-hash in my case) like tineye. Googles aproach is not open for the public to know, but probably uses machine Learning and pattern matching. From my own experience the algorithm im using is very fast (approx 1s-2s on a 1ghz single core cpu for one hash+ approx 0.2s for listing similar hashes from a database containing approx 0.5 million hashes, the database is subject to change and is missing many pictures from steemit). But the downside is that i can only find identical pictures and slightly edited pictures, whereas Google can is very good at finding similar pictures, due to machine Learning. Note that i do this project just for fun for me to learn database handling, pictureprocessing and multiprocessing.

It sounds like a great project. I didn't mean to criticize you, just wondered about the details. Even if it is not growing into something big, it will still be a great project to work on and learn from.

Finding similar images would be key though, as people tend to adjust 'stolen' images a little to make them look their own.

Well it works to a certain extent. Atm the Problem is the database structure, because the hashes are saved to sql where i only can check if they are exactly the same. To look for similar i would need to compute the hamming distance which is very slow because I need to compute it for every other hash in the database, which would be very slow. Therefore i need to expirement with b-trees.

Not sure if it is possible as I have not tried anything related before. But if you could just save the middle of the image somehow, you might be able to make a good comparison.

Well to produce a hash images are scaled down to a picture with a Pixel amount from the Power of 2 (64 pixels being the smallest with good results). Before and After resizing certain Operations are applied to get better results. Depending on the Operations used the accuracy and time to compute changes. Sample operations are, convert to grayscale, Discrete wavelet transform, Discrete cosine transform, etc. There is a Python libary that i am using : https://github.com/JohannesBuchner/imagehash
On that github repro are also links to webpages on how they work and how effective they are.
Due to these algorithims slight changes like jpeg compression artifacts, rescaling, slight cropping do not affect the hash that much. Cropping does still affect the hash the most ill try out if your Idea or similar techniques work.

Keep in mind I am just thinking out loud there. My idea is that changes made to an image mostly happen at the top and bottom. If it is possible to just check some area in the middle you could find equal and adjusted versions

Thank you very much for your support. It is so important for a photographer to know that his efforts are not in vain.

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Sounds good, best wishes on your endeavor.

Thank you for the support 😀🐉🐲💗

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