Intopii

1. Collect sample images

Start by collecting a representative set of sample images about your problem. The more the better but you can start with just a few tens of them. You don't need to categorize them beforehand - Pinta will take care about that. That's the beauty of unsupervised learning.

2. Create your analyzer

Launch Pinta Trainer. Create your analyzer by selecting features that measure the important things and let Pinta learn from the samples. Evaluate the result and assign the classes you want to divide the incoming images into using an intuitive visual image map. Save your work to get a configuration file (.cft) that contains all the image analyzing intelligence you just created.

3. Deploy the analyzer

Integrate the trained analyzer with your application via the simple and well-defined Pinta API. Load your configuration and feed it with image data to get results. Based on the result, your application can sort, categorize, and detect images or trigger other actions. Anything you need.

Let's assume that you have images you want to categorize based on their general appearance. Here's what to do:

1. Start Pinta Trainer.
2. Select a bunch of images you want to analyze using "Browse..." on the left.
3. Hit "Build" in the toolbar.
4. Wait until Pinta finishes learning and images show up on the map on the right.
5. Check the results by double clicking on the cells on the map. Similar images should be in the same or nearby cells.
6. Play with the parameters to see how they affect the results.
7. When satisfied, label the map as you wish using "Edit metrics..." above the map and assigning each cell an appropriate label.
8. Save the analyzer.

For detailed instructions, please refer to the manual.