Measuring similarity between two images

1. How can we measure similarities between two images?
2. How do you evaluate image similarity?
3. How do you quantify the difference between two images?
4. How do you compare two images and calculate relevance?

How can we measure similarities between two images?

The subjective similarity between two pictures is quantified in terms of a distance measure which is defined on the corresponding multi-dimensional feature space. Common distance measures are: the Minkowski distance, the Manhattan distance, the Euclidean distance and the Hausdorff distance.

How do you evaluate image similarity?

To quantify image similarity several measures have been proposed. Common choices for include sum of squared differences (SSD), mutual information (MI) (Collignon et al., 1995; Viola and Wells III, 1997), normalized mutual information (NMI) (Studholme et al., 1999), or cross-correlation (CC).

How do you quantify the difference between two images?

The difference between two images is calculated by finding the difference between each pixel in each image, and generating an image based on the result.

How do you compare two images and calculate relevance?

To compare two images, we use the Mean Square Error (MSE) of the pixel values of the two images. Similar images will have less mean square error value. Using this method, we can compare two images having the same height, width and number of channels.