deeplearning / LargescaleImageMemorability / 0.1.3
README.md
1. Introduction
This algorithm tells you have memorable an image is to humans. The algorithm reaches a rank correlation of 0.64, near human consistency (0.68).
Input:
- (Required) Image Data API Url, Web (http/https) Url, binary image or a base64 encoded image.
Output:
- Image memorability
Note: The first call to this algorithm will take a bit longer than sequential calls to due algorithm initialization. All following calls will be significantly faster.
2. Examples
Example 1.
- Parameter 1: Data API Url
{
"image": "data://deeplearning/example_data/garden.jpeg"
}
Output
{
"memorability": 0.6875075697898865
}
Example 2.
- Parameter 1: HTTP Url
{
"image": "https://s3.amazonaws.com/algorithmia-assets/algo_desc_images/deeplearning_RealEstateClassifier/beach_house.jpeg"
}
Output:
{
"memorability": 0.5041214823722839
}
Example 3.
- Parameter 1: Base64 image
{
"image": "data:image/png;base64....",
}
Output;
{
"memorability": 0.3471947391028374
}
3. Credits
for more information, please refer to: http://memorability.csail.mit.edu/ or Understanding and Predicting Image Memorability at a Large Scale A. Khosla, A. S. Raju, A. Torralba and A. Oliva International Conference on Computer Vision (ICCV), 2015 DOI 10.1109/ICCV.2015.275
Demo is was taken from:
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