Image segmentation

Image segmentation is a method of dividing a digital image into subgroups called image segments, reducing the complexity of the image and enabling further processing or analysis of each image segment. Technically, segmentation is the assignment of labels to pixels to identify objects, people, or other important elements in the image.?

A common use of image segmentation is in object detection. Instead of processing the entire image, a common practice is to first use an image segmentation algorithm to find objects of interest in the image. Then, the object detector can operate on a bounding box already defined by the segmentation algorithm. This prevents the detector from processing the entire image, improving accuracy and reducing inference time.

Image segmentation is a key building block of computer vision technologies and algorithms. It is used for many practical applications including medical image analysis, computer vision for autonomous vehicles, face recognition and detection, video surveillance, and satellite image analysis.

How does image segmentation work?

Image segmentation is a function that takes image inputs and produces an output. The output is a mask or a matrix with various elements specifying the object class or instance to which each pixel belongs.

Several relevant heuristics, or high-level image features, can be useful for image segmentation. These features are the basis for standard image segmentation algorithms that use clustering techniques like edges and histograms.

An example of a popular heuristic is color. Graphics creators may use a green screen to ensure the image background has a uniform color, enabling programmatic background detection and replacement in post-processing.?

Another example of a useful heuristic is contrast—image segmentation programs can easily distinguish between a dark figure and a light background (i.e., the sky). The program identifies pixel boundaries based on highly contrasting values.

Traditional image segmentation techniques based on such heuristics can be fast and simple, but they often require significant fine-tuning to support specific use cases with manually designed heuristics. They are not always sufficiently accurate to use for complex images. Newer segmentation techniques use machine learning and deep learning to increase accuracy and flexibility.?

Machine learning-based image segmentation approaches use model training to improve the program’s ability to identify important features. Deep neural network technology is especially effective for image segmentation tasks.?

There are various neural network designs and implementations suitable for image segmentation. They usually contain the same basic components:

• An encoder—a series of layers that extract image features using progressively deeper, narrower filters. The encoder might be pre-trained on a similar task (e.g., image recognition), allowing it to leverage its existing knowledge to perform segmentation tasks.
• A decoder—a series of layers that gradually convert the encoder’s output into a segmentation mask corresponding with the input image’s pixel resolution.?
• Skip connections—multiple long-range neural network connections allowing the model to identify features at different scales to enhance model accuracy.