Today we are discussing color segmentation, which can be automated with the assistance of ML experts and software developers. This solution can be useful for recognizing the elements of a person’s clothing and the construction of a further recommendation system based on his style, for the automatic construction of maps, using a satellite image, and in many other cases, which we will mention in this article. Color segmentation becomes more and more relevant with every passing year, as it is essential for AI technologies to be able to recognize the specific parts of the image.
Some developers use ready-made neural networks for this purpose, which should be periodically retrained after the emergence of new data to ensure a better quality of work. Neural networks need marked-up data for training, which may be impractical or impossible in some tasks. The advantage of clustering methods is that initially, the clustering is done without a teacher, i.e. it does not require any prepared data. K-means clustering is probably the most well-known algorithm for color segmentation.
Color segmentation
Color segmentation divides a digital image into several sets of pixels, also known as superpixels, with similar attributes. The purpose is to change an image representation into something more meaningful and easier to analyze – a set of colors.
More precisely, color segmentation is assigning a label to each pixel in an image so that pixels with the same label have certain characteristics.
Why does color segmentation matter?
If we take autonomous vehicles as an example, they need sensor input devices (cameras, radars, and lasers) to enable the vehicle to perceive the world around it. Autonomous driving is impossible without object detection, which involves image classification/segmentation.
Other examples include the healthcare industry where, if we are talking about cancer, even in the current age of technological advances, cancer can be fatal if we do not identify it at an early stage. Detecting cancer cells as quickly as possible could save millions of lives. The shape and color of cancer cells on the scans play a vital role in determining the severity of the disease, which is identified using classification algorithms.
Over the years, several algorithms and techniques for color and image segmentation have emerged. One of them, and the one widely used today, is K-means.
K-means Сlustering
Clustering algorithms are unsupervised algorithms, similar to classification algorithms, but the basis is different. In clustering, you do not know what you are looking for and try to identify clusters in your data. When you use clustering on your dataset, unexpected things like structures, clusters, and groupings that you would never have thought of may suddenly appear.
K-Means clustering is used to separate the area of interest from the background. It groups or divides the data into K clusters or parts based on K-centroids.
It’s used when you have unmapped data (data without defined categories or groups). The goal is to find groups based on some similarity between the data and the number of groups represented by K.
K-means clustering minimizes the sum of the square distances between all points and the center of the cluster.
Steps of the clustering:
- Select the number of clusters.
- Choose random points (not necessarily from your dataset).
- Correspond data points with the nearest centroid.
- Calculate and assign a new centroid to each cluster.
- Reassign each data point to the new centroid.
For a specific class of clustering algorithms, there is a parameter (usually called K) that determines the number of clusters to detect. Other algorithms, such as DBSCAN and OPTICS, do not require this parameter since hierarchical clustering avoids the problem altogether.
Speaking of K-means, the choice of K often depends on the shape/scale of the point distribution in the dataset and the clustering resolution. Furthermore, increasing K will always reduce the number of errors in the resulting clustering, sometimes even to zero, if each data point is treated as a cluster (i.e., when K equals the number of data points).
Conclusion
Thanks to advances in color processing, machine learning, artificial intelligence, and related technologies, in a few decades the world will have millions of solutions that will change our lives. These will include verbal commands, anticipating the information needs of governments, translating languages, recognizing and tracking objects, diagnosing diseases, performing operations, and many others. Real-life applications are endless.
