Rules based on the manual visualization of an image’s object color – Labeling Image Data Using Rules

In this section, let us see how we can use LFs that look for specific visual features that are characteristic of images of a plant’s leaves, which we are interested in classifying as “healthy” or “deceased”. For instance, we could use an LF that checks whether the image has a certain color distribution, or whether it contains specific shapes that are common in those images.

Snorkel’s LFs can be used to label images based on various properties, such as the presence of certain objects, colors, textures, and shapes. Here’s an example of Python code that uses Snorkel LFs to detect images based on their color distribution.

Creating labeling rules based on manual inspection of image visualizations is a manual process that often involves the expertise of a human annotator. This process is commonly used in scenarios where there is no existing labeled dataset, and you need to create labels for machine learning or analysis tasks.

Here’s a general outline of how you can create labeling rules based on the manual inspection of image visualizations in Python:

1. Collect a representative sample: Begin by selecting a representative sample of images from your dataset. This sample should cover the range of variations and categories you want to classify.
2. Define the labeling criteria: Clearly define the criteria or rules to label images based on their visual properties. For example, if you’re classifying images to identify plant diseases from images of leaves, agricultural experts visually inspect leaf images for discoloration, spots, or unusual patterns. Rules can be defined based on the appearance and location of symptoms. We will use this example for our demonstration shortly.
3. Create a labeling interface: You can use existing tools or libraries to create a labeling interface where human annotators can view images and apply labels based on the defined criteria. Libraries such as Labelbox and Supervisely or custom interfaces, using Python web frameworks such as Flask or Django, can be used for this purpose.
4. Annotate the images: Have human annotators manually inspect each image in your sample and apply labels according to the defined criteria. This step involves the human annotators visually inspecting the images and making classification decisions, based on their expertise and the provided guidelines.
5. Collect annotations: Collect the annotations generated by the human annotators. Each image should have a corresponding label or class assigned based on the visual inspection.
6. Analyze and formalize rules: After collecting a sufficient number of annotations, analyze the patterns and decisions made by the annotators. Try to formalize the decision criteria based on the annotations. For example, you might observe that images with certain visual features were consistently labeled as a specific class.
7. Convert rules to code: Translate the formalized decision criteria into code that can automatically classify images based on those rules. This code can be written in Python and integrated into your machine learning pipeline or analysis workflow.
8. Test and validate rules: Apply the automated labeling rules to a larger portion of your dataset to ensure that they generalize well. Validate the rules by comparing the automated labels with ground truth labels if available, or by reviewing a subset of the automatically labeled images manually.
9. Iterate and refine: Iteratively refine the labeling rules based on feedback, error analysis, and additional manual inspection if necessary. This process may involve improving the rules, adding more criteria, or adjusting thresholds.

Creating labeling rules based on manual inspection is a labor-intensive process but can be essential to generate labeled data when no other options are available. The quality of your labeled dataset and the effectiveness of your rules depend on the accuracy and consistency of the human annotators, as well as the clarity of the defined criteria.