Image Similarity in Percentage %
Siamese network to compare image similarity in percentage - based on Keras deep learning model (VGG16, ResNet50) & cosine similarity, euclidean similarity
Accuracy
The cosine similarity and euclidean similarity are shown in the table.
| image1 | image2 | cosine similarity (VGG16) | euclidean similarity (VGG16) | cosine similarity (ResNet50) | euclidean similarity (ResNet50) |
|---|---|---|---|---|---|
 |
 |
84.51% | 0.01326 | 91.28% | 0.05116 |
| --- | --- | --- | --- | --- | --- |
 |
 |
63.95% | 0.00980 | 54.98% | 0.02871 |
| --- | --- | --- | --- | --- | --- |
 |
 |
100.00% | 1.0 | 100.00% | 1.0 |
| --- | --- | --- | --- | --- | --- |
|
 |
63.66% | 0.01222 | 78.96% | 0.03771 |
| --- | --- | --- | --- | --- | --- |
|
 |
51.74% | 0.01105 | 51.18% | 0.02189 |
| --- | --- | --- | --- | --- | --- |
 |
|
23.80% | 0.00907 | 30.91% | 0.01755 |
| --- | --- | --- | --- | --- | --- |
 |
 |
42.68% | 0.01361 | 49.00% | 0.02593 |
| --- | --- | --- | --- | --- | --- |
 |
 |
69.20% | 0.01478 | 70.07% | 0.02849 |
| --- | --- | --- | --- | --- | --- |
 |
 |
77.01% | 0.02064 | 82.51% | 0.04565 |
| --- | --- | --- | --- | --- | --- |
 Original Image |
 Cropped Image |
93.75% | 0.03695 | 95.31% | 0.07801 |
| --- | --- | --- | --- | --- | --- |
 Original Image |
 Adversarial Image |
74.47% | 0.01384 | 90.14% | 0.06188 |
| --- | --- | --- | --- | --- | --- |
 Original Image |
 Adversarial Image |
79.60% | 0.01324 | 91.45% | 0.04503 |
| --- | --- | --- | --- | --- | --- |
 Original Image |
 Screenshot Image |
97.95% | 0.06415 | 98.69% | 0.13120 |
License
- This image similarity model is developed based on Keras deep learning model from https://github.com/fchollet/deep-learning-models
- The ResNet50 weights are ported from the ones released by Kaiming He under the MIT license.
- The VGG16 and VGG19 weights are ported from the ones released by VGG at Oxford under the Creative Commons Attribution License.
- The Inception v3 weights are trained by ourselves and are released under the MIT license.
- Thanks to DAmageNet: A Universal Adversarial Dataset https://arxiv.org/abs/1912.07160
Citations
@inproceedings{Panagiotis2021,
author = {Panagiotis Kasnesis, Ryan Heartfield, Xing Liang, Lazaros Toumanidis, Georgia Sakellari, Charalampos Patrikakis, George Loukas},
booktitle = {Journal of Applied Soft Computing},
title = {Transformer-based identification of stochastic information cascades in social networks using text and image similarity},
year = {2021}
}