Matching images using Image Hashing

This is a brief post of my notes describing the process to match similar images in an archive of photographs. I am using the techniques described by Adrian Rosebrock in his excellent article Image hashing with OpenCV and Python. The images used are from the Pompeii Artistic Landscape Project and provided courtesy of Pompeii in Pictures.

Image hashing is a process to match images through the use of a number that represents a very simplified form of an image, like this one below.

Original image before image hashing. Images courtesy of Pompeii in Pictures.

First, the color of the image is simplified. The image is converted to grayscale. See below:

Image converted to grayscale. Images courtesy of Pompeii in Pictures.

Next, the image is simplified by size. It is resized 9 pixels wide by 8 pixels high.

Image resized to 9 pixels wide by 8 pixels high. The green and red rectangles are relevant to describe the next step.

Adrian Rosebrock uses a differential hash based on brightness to create a binary number of 64 bits. Each bit is 1 or 0. Two pixels next to each other horizontally are compared: left and right. If right is brighter, bit = 1. Bit = 0 if left is brighter. See below:

The result of the image hash for the image above. The 1 inside the green square is the result of the comparison between the 2 pixels in the green rectangle in the picture above. The same thing is true for the o inside the red square. Inside the red rectangle two images above, the pixel on the left is brighter, so 0 is the result.

This process produces a 64bit binary number: 0101001100000011101001111000101110011101000011110000001001000011

This converts to decimal 5981808948155449923.


A match of copies of an image.
An interesting match of similar images.


Dunn, Jackie and Bob Dunn. Pompeii in Pictures.

Rosebrock, Adrian. Image hashing with OpenCV and Python.

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