Significance of Image Labeling in AI

Author(s): Rayan Potter

Originally published on Towards AI.

The capability of AI to see and perceive its surroundings has myriad advantages. In this blog, we will explore further the invaluable role image labeling plays in training AI to see like humans.

Image labeling plays an invaluable role in AI by training machine learning models to identify an image and classes of objects within it. It plays a significant role across diverse industries by assisting organizations in decision-making.

It also enhances the accuracy and efficacy of AI algorithms. It helps in training machine learning models by extracting key information for computer vision models regarding the objects present in an image.

Image labeling is undoubtedly the driving force behind advanced technologies, including robotics, autonomous vehicles, medical imaging, and more. All these technologies become alive through image labeling.

Let’s dive into the blog below to understand the key aspects of image labeling.

Role of Image Labeling in AI

Image labeling involves the identification and marking of raw data like images, videos, texts, and more for training machine learning models. It helps in adding informative and meaningful labels to images to add context and aid machine learning models to learn from it.

Image labeling plays two critical roles in AI:

• Develop working AI models: Tools and techniques in image labeling assist with highlighting or capturing key objects within an image. The labels aid in making images readable to machines. The highlighted images are used as training datasets for AI and machine learning models.
• Enhance computer vision: Image captions and annotations enhance accuracy through object detection. AI models can identify patterns by training AI and machine learning with labels.

Techniques in Image Labeling

Images need to be labeled accurately for training neural networks. There are three main techniques in image labeling:

Manual image labeling

This method requires manually defining labels for the whole image by drawing regions within an image and text descriptions for each area. This technique requires a human labeler to examine the image carefully, identify the objects, draw bounding boxes or polygons around the objects, and assign labels to every object. However, this technique suffers from two key limitations: labeling inconsistency and scalability.

Semi-automated image labeling

This technique of image labeling aids manual labelers by detecting the boundaries of objects within an image by offering a starting point to them. Image annotation software saves human labelers’ precious time by providing a partial map of objects in the image. This technique is useful when large datasets are involved as it hastens the labeling process without affecting accuracy.

Types of Image Labeling

There are eight types of image labeling as outlined below:

Image Classification

Image classification algorithms acquire images as input and automatically classify them into one of many labels or classes. A training dataset for image classification involves manually reviewing images and annotating them using labels via the algorithm.

Semantic Segmentation

This technique is used in computer vision for segmenting images. An image dataset is semantically segmented for locating all categories and classes.

Object Detection

An algorithm is used to detect an image within an image along with its location within an image frame. The area is indicated using various shapes, such as facial recognition dots used in facial recognition systems.

Skeletal Annotation

This technique is used to highlight body movement and alignment. Annotators use this technique for connecting lines on the human body. Dots are used to connect them at points of articulation.

2D Bounding Boxes

Through graphical representations, boundaries of objects are defined in a two-dimensional space. These boxes are used in computer vision and machine learning applications to segregate areas of interest for objects.

Key Point Annotation

This annotation technique is used for recognizing facial gestures, human poses, expressions, emotions, body language, and sentiments through connection of multiple dots.

Polygon Annotation

This technique involves marking and drawing shapes on a digital image as per their position and orientation. It also involves labeling images of irregular dimensions.

3D Cuboid Annotation

This technique involves the detection and recognition of 3D objects in images. It assists machines in estimating the depth of objects like vehicles, people, buildings, and other objects.

Use cases of Image Labeling

Image labeling helps optimize real-life operations by training computers to interpret and comprehend the visual world the way humans do.

Retail

Image labeling using the 2D bounding box technique is used for labeling images in retail stores, including shirts, trousers, jackets, persons, etc. It helps in training machine learning models on diverse features, including price, color, design, etc.

Healthcare

Human organs in X-rays are labeled using the polygon technique. Machine learning models acquire training to identify deformities in human X-rays. Image labeling revolutionizes healthcare by spotting diseases, reducing costs, and enhancing patient experience.

Self-Driving or Autonomous Vehicles

Several car makers are adopting this technology, which depends on Semantic segmentation to label every pixel of an image. It helps identify roads, cars, traffic lights, poles, pedestrians, etc. It also helps make vehicles aware of their surroundings and sense obstacles in their path.

Emotion Detection

Human emotions or sentiments are detected using landmark annotation. This measures a person’s emotional state in a given piece of content. It helps interpret product reviews, service reviews, movie reviews, email complaints/feedback, customer calls, meetings, and more.

Supply Chain

The lines and splines technique is used to label lanes within warehouses. This helps identify tracks according to their delivery location. It also assists robots in optimizing their path and automating the delivery chain, reducing human intervention and errors.

Image Labeling Services Outsourcing

The success of any AI and ML model depends on qualitative and accurate training datasets. Outsourcing image labeling services is an economical and efficient way for companies to handle their data training requirements.

Each image is labeled precisely to help ML algorithms detect and identify objects readily. Image labeling services assist in offering original data for building and optimizing AI models. By properly selecting the right image labeling service provider, businesses can reap the rewards of computer vision and AI-based solutions.

Key Benefits of Outsourcing Image Labeling Services

Advancements in AI and ML for positive results

Image labeling service providers specialize in labeling practices, so they are abreast of advancements in AI and ML models. They offer high-quality labeled images to ensure the AI model delivers accurate results. Better labeling enhances the AI model’s precision, resulting in positive ML results.

Unique and customized solutions for quality product development

The exposure to various business use cases helps in delivering unique and personalized solutions that cater to any AI need.

Automation and scalability for efficient business operations

Image labeling service providers offer an automated approach by minimizing the use of rulers and inspecting them. It helps in saving time and costs. Outsourcing helps in scaling without consuming the company’s local resources.

Competitive advantage

AI assists companies in gaining an edge by enhancing their position among competitors. Labeled images help in deriving better data insights, which in turn results in strategizing.

Conclusion

Outsourcing image labeling services is a viable option for businesses today as it helps them enhance their operational efficiency and expand their reach to various applications, such as autonomous vehicles and medical imaging.

The labeling of images and videos has enabled businesses to perform real-time analysis. What remains to be done is to allow machines to imagine and understand problems to be solved, along with a partner like machine learning to guide businesses through this intricate lifecycle.

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Published via Towards AI