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for my school project, I need to find images in a large dataset. I'm working with python and opencv. Until now, I've managed to find an exact match of an image in the dataset but it takes a lot of time even though I had 20 images for the test code. So, I've searched few pages of google and I've tried the code on these pages

image hashing

building an image hashing search engine

feature matching

Also, I've been thinking to search through the hashed dataset, save their paths, then find the best feature matching image among them. But most of the time, my narrowed down working area is so much different than what is my query image.

The image hashing is really great. It looks like what I need but there is a problem: I need to find an exact match, not similar photos. So, I'm asking you guys, if you have any suggestion or a piece of code might help or improve the reference code that I've linked, can you share it with me? I'd be really happy to try or research what you guys send or suggest.

Onur Baskin
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  • can manhattan distance or simple image subtraction solve your problem? I am not sure. I am just suggesting. Like have you tried with that? And exact images will have all their pixel values same. So subtracting or getting the manhattan distance should give the resultant output to be zero. – em_bis_me Nov 26 '19 at 14:13
  • @em_bis_me the thing is, if you have checked the linked that I gave, you can see that the author is almost doing the same thing as you suggested. But my issue is a little more complicated than that. When I present my project, probably we are gonna test it with the picture which we are gonna take on spot. so yeah it might be the same item but it won't have the same pixel values, width or height. – Onur Baskin Nov 26 '19 at 18:09

2 Answers2

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opencv is probably the wrong tool for this. The algorithms there are geared towards finding similar matches, not exact ones. The general idea is to use machine learning to teach the code to recognize what a car looks like so it can detect cars in videos, even when the color or form changes (driving in the shadow, different make, etc).

I've found two approaches work well when trying to build an image database.

  1. Use a normal hash algorithm like SHA-256 plus maybe some metadata (file or image size) to find matches

  2. Resize the image down to 4x4 or even 2x2. Use the pixel RGB values as "hash".

The first approach is to reduce the image to a number. You can then put the number in a look up table. When searching for the image, apply the same hashing algorithm to the image you're looking for. Use the new number to look in the table. If it's there, you have a match.

Note: In all cases, hashing can produce the same number for different pictures. So you have to compare all the pixels of two pictures to make sure it's really an exact match. That's why it sometimes helps to add information like the picture size (in pixels, not file size in bytes).

The second approach allows to find pictures which very similar to the eye but in fact slightly different. Imagine cropping off a single pixel column on the left or tilting the image by 0.01°. To you, the image will be the same but for a computer, they will by totally different. The second approach tries to average small changes out. The cost here is that you will get more collisions, especially for B&W pictures.

Aaron Digulla
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Finding exact image matches using hash functions can be done with the undouble library (Disclaimer: I am also the author). It works using a multi-step process of pre-processing the images (grayscaling, normalizing, and scaling), computing the image hash, and the grouping of images based on a threshold value.

erdogant
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