Euclidean distance and Euclidian Similarity for Images - Python for Integrated Circuits - - An Online Book - |
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Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||||||||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||||||||||||||
================================================================================= The algorithm to find the similarity between the two images can be: ============================================ Euclidean distance and Euclidian Similarity for images. One limitation of Euclidean distance is that it requires all the vectors to be normalized, that is, both the vectors for comparison need to be of the same dimensions. To ensure that the histogram vectors are normalized, the images are resized to a specific size which is smaller than the size of all the images (note that error can occur if the specific image size is larger than one or more images in the group for similarity comparison). Finally, script returns a dictionary where the key corresponds to the pixel value and the value of the key is the number of times that pixel is present in the image. code:
Output (the final vectors for both the reference images and the test image enables to calculate the distances and predict the image similarity, that is, the smaller than distance is, the more similarity is): ![]()
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