Labeling Image Data Using Rules

In this chapter, we will explore data labeling techniques tailored specifically for image classification, using Python. Our primary objective is to clarify the path you need to take to generate precise labels for these images in the dataset, relying on meticulously crafted rules founded upon various image properties. You will be empowered with the ability to dissect and decode images through manual inspection, harnessing the formidable Python ecosystem.

In this chapter, you will learn the following:

• How to create labeling rules based on manual inspection of image visualizations in Python
• How to create labeling rules based on the size and aspect ratio of images
• How to apply transfer learning to label image data, using pre-trained models such as YOLO V3

The overarching goal is to empower you with the ability to generate precise and reliable labels for your data. We aim to equip you with a versatile set of labeling strategies that...

Technical requirements

Complete code notebooks for the examples used in this chapter are available on GitHub at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python.

The sample image dataset used in this chapter is available on GitHub at https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python/tree/main/images.

Labeling rules based on image visualization

Image classification is the process of categorizing an image into one or more classes based on its content. It is a challenging task due to the high variability and complexity of images. In recent years, machine learning techniques have been applied to image classification with great success. However, machine learning models require a large amount of labeled data to train effectively.

Image labeling using rules with Snorkel

Snorkel is an open source data platform that provides a way to generate large amounts of labeled data using weak supervision techniques. Weak supervision allows you to label data with noisy or incomplete sources of supervision, such as heuristics, rules, or patterns.

Snorkel primarily operates within the paradigm of weak supervision rather than traditional semi-supervised learning. Snorkel is a framework designed for weak supervision, where the labeling process may involve noisy, limited, or imprecise rules rather...

Labeling images using rules based on properties

Let us see an example of Python code that demonstrates how to classify images using rules, based on image properties such as size and aspect ratio.

Here, we will define rules such as if the black color distribution is greater than 50% in leaves, then that is a diseased plant. Similarly, in case of detecting a bicycle with a person, if the aspect ratio of an image is greater than some threshold value, then that image has a bicycle with a person.

In computer vision and image classification, the aspect ratio refers to the ratio of the width to the height of an image or object. It is a measure of how elongated or stretched an object or image appears along its horizontal and vertical dimensions. Aspect ratio is often used as a feature or criterion in image analysis and classification. It’s worth noting that aspect ratio alone is often not sufficient for classification, and it is typically used in conjunction with other features...

Labeling images using transfer learning

Transfer learning is a machine learning technique where a model trained on one task is adapted for a second related task. Instead of starting the learning process from scratch, transfer learning leverages knowledge gained from solving one problem and applies it to a different but related problem. This approach has become increasingly popular in deep learning and has several advantages:

• Faster training: Transfer learning can significantly reduce the time and computational resources required to train a model. Instead of training a deep neural network from random initialization, you start with a pre-trained model, which already has learned features and representations.
• Better generalization: Models pre-trained on large datasets, such as ImageNet for image recognition, have learned general features that are useful for various related tasks. These features tend to generalize well to new tasks, leading to better performance.
• Lower data...

Labeling images using transformations

In this section, let us see the different types of transformations that can be applied to images to generate synthetic data when there is a limited amount of data. In machine learning, shearing and flipping are often used as image augmentation techniques to increase the diversity of training data. It helps improve a model’s ability to recognize objects from different angles or orientations.

Shearing can be used in computer vision tasks to correct for perspective distortion in images. For example, it can be applied to rectify skewed text in scanned documents.

Image shearing is a transformation that distorts an image by moving its pixels in a specific direction. It involves shifting the pixels of an image along one of its axes while keeping the other axis unchanged. There are two primary types of shearing:

• Horizontal shearing: In this case, pixels are shifted horizontally, usually in a diagonal manner, causing an image to slant...

Summary

In this chapter, we embarked on an enlightening journey into the world of image labeling and classification. We began by mastering the art of creating labeling rules through manual inspection, tapping into the extensive capabilities of Python. This newfound skill empowers us to translate visual intuition into valuable data, a crucial asset in the realm of machine learning.

As we delved deeper, we explored the intricacies of size, aspect ratio, bounding boxes, and polygon and polyline annotations. We learned how to craft labeling rules based on these quantitative image characteristics, ushering in a systematic and dependable approach to data labeling.

Our exploration extended to the transformative realm of image manipulation. We harnessed the potential of image transformations such as shearing and flipping, enhancing our labeling process with dynamic versatility.

Furthermore, we applied our knowledge to real-world scenarios, classifying plant disease images using rule...