Semantic segmentation is an image-labeling technique to create data for supervised computer vision (CV) models.
In its simplest terms, the objective is to assign a class label to each pixel in an image.
For example, if you’re labeling a cat against a plain background, semantic segmentation labels every single pixel of the cat as a “cat”.
Pixel-level labels are required for image segmentation tasks, which differs from object detection.
Object detection aims to locate objects within an image, predicting bounding boxes or polygons, whereas image segmentation aims to predict the boundaries between objects, predicting masks.
Semantic segmentation is the most common form of data annotation for segmentation tasks.
This article will explain semantic segmentation in detail.
Semantic segmentation is the process of partitioning an image into semantically meaningful regions and assigning a class label to each pixel, thus forming groups of pixels.
This enables computer vision algorithms to understand the context and relationships between different objects in a scene at a granular level.
Semantic segmentation differs from other computer vision techniques, such as image classification, object detection, and instance segmentation. Image classification assigns a single label to an entire image, while object detection locates and classifies individual objects.
Semantic segmentation is related to instance and panoptic segmentation, which go a step further by identifying different instances within a class.
For example, with instance segmentation, you could have a class “human”, and label every instance of humans as human 1, human 2, etc. In semantic segmentation, humans would simply be labeled as humans – there’s no distinction between instances.
For more information on different approaches to computer vision, read this article.
Aya Data used semantic segmentation to label maize leaves for a maize disease identification model - read more in the case study.
The goal was to assist farmers in identifying diseases and recommending treatment options.
To train the model, Aya Data labeled digital photographs of real maize diseases to build a training dataset. Local agronomists helped us identify specific diseases, enabling us to label them accurately.
The dataset consisted of 5000 high-quality images labeled using semantic segmentation. Semantic segmentation allowed the identification of diseased leaves and class labeling of diseases. See an example of a labeled leaf below.
The resulting dataset facilitated the training of a computer vision model capable of detecting common maize diseases with 95% accuracy.
Farmers could access the model through an app, which provides information on the disease type, severity, and treatment options.
Semantic segmentation is ideal here, as it enabled us to label the entire leaf with the disease class. Bounding boxes or polygons wouldn't have been as effective, and there was no need for instance segmentation as each image contains only one instance of the leaf.
Labeling images for semantic segmentation typically use graph-based methods, region merging, and clustering.
This sounds complex, but grouping distinctive shapes at the pixel-level using modern labeling tools is relatively straightforward.
If you’ve used Photoshop or another image editor, the process is similar to using the “Magic Wand” tool that automatically finds the boundary between shapes. Then, each pixel within that pixel mask is labeled.
Automatic labeling techniques have also been used to segment images based on features such as color, texture, and shape.
In terms of data annotation, semantic segmentation is more time-consuming than other methods like bounding boxes.
Some computer vision applications require large-scale, diverse, well-annotated datasets with high-quality, pixel-level annotations - which is labor-intensive. Class imbalance and the presence of rare classes complicate the process further.
Semantic segmentation is an extremely useful method for labeling images for supervised computer vision projects.
While the fundamental goal is to create data that models can understand, it differs from other labeling methods, such as image classification, object detection, and instance segmentation.
Here's a comparison of these common image labeling techniques:
In image classification, the goal is to assign a single label to the entire image, representing its primary content or category. For example, an image with a cat against a backdrop of various household items would simply be labeled as a "cat", if the cat was the feature relevant to the model.
This technique does not provide any information about the location or shape of the objects within the image. It’s the simplest form of image labeling.
Simple object detection aims to locate and classify multiple objects within an image. It typically uses bounding boxes to enclose each identified object and label it.
While object detection provides information about the location and classification of objects, it does not precisely outline their shapes or separate overlapping instances.
For non-linear or complex shapes, bounding boxes can be replaced by polygons or key points.
As mentioned, semantic segmentation involves assigning a class label to each pixel in the image, providing a detailed understanding of object shapes and locations.
This method generates a pixel-level mask for each object class, but it does not distinguish between separate instances of the same class.
For example, suppose multiple cars of the same class appear in an image. In that case, semantic segmentation will label all their pixels with the same class, making it impossible to differentiate individual cars.
Semantic segmentation is ideal for accurately labeling complex or non-linear shapes.
Instance segmentation is similar to semantic segmentation in that it labels individual pixels.
However, instance segmentation goes a step further by differentiating between separate instances of the same class. For example, if there are multiple cars in an image, instance segmentation will assign different labels to each car, even if they are of the same class.
Panoptic segmentation aims to assign a class label to every pixel in an image while also distinguishing between separate instances of the same class.
This method provides a unified approach to segmentation, where both the "stuff" classes (e.g., sidewalk, sky, grass, water) and the "thing" classes (e.g., car, cat, person, dog) are labeled at the pixel level, with individual instances being identified for "thing" classes.
Semantic segmentation has numerous applications across various CV domains:
In essence, any supervised CV task that requires accurate pixel masks without needing to segment instances can apply semantic segmentation.
Semantic segmentation enables computers to understand and interpret complex visual scenes.
Semantic segmentation is key to training CV models for the purposes of autonomous vehicles, medical imaging, etc.
As Aya Data has demonstrated, semantic segmentation has novel and creative applications, e.g., in the training of a maize disease detection model.
Contact Aya Data for your data labeling needs.
Human annotators play a critical role in semantic annotation for computer vision projects. They are responsible for accurately labeling each pixel in an image with the corresponding class or category it belongs to.
Human expertise uses automated tools to simplify the process. Domain specialists and experts can also contribute, ensuring the dataset is accurate and appropriate for the model.
Semantic annotation is essential for computer vision projects as it provides detailed information about the objects and their boundaries in an image, enabling the model to understand and recognize complex scenes.
Semantic annotation allows the model to accurately distinguish between different objects and their shapes by assigning a class label to each pixel.
Semantic annotation focuses on assigning a class label to each pixel in an image, resulting in a more detailed understanding of object shapes and locations.
In contrast, other forms of image annotation, such as image classification, object detection, instance segmentation, and panoptic segmentation, have different objectives and levels of granularity.
For example, semantic segmentation is more advanced than simple bounding boxes for object detection but less sophisticated than instance and panoptic segmentation.