Using the Watershed algorithm for video data labeling – Labeling Video Data

The Watershed algorithm is a popular technique used for image segmentation, and it can be adapted to label video data as well.

It is particularly effective in segmenting complex images with irregular boundaries and overlapping objects. Inspired by the natural process of watersheds in hydrology, the algorithm treats grayscale or gradient images as topographic maps, where each pixel represents a point on the terrain. By simulating the flooding of basins from different regions, the Watershed algorithm divides the image into distinct regions or segments.

In this section, we will explore the concept of the Watershed algorithm in detail. We will discuss its underlying principles, the steps involved in the algorithm, and its applications in various fields. Additionally, we will provide practical examples and code implementations to illustrate how the Watershed algorithm can be applied to segment and label video data.

The algorithm works by treating an image as a topographic surface and considering the grayscale intensity or gradient information as the elevation. This algorithm uses the concept of markers, which are user-defined points that guide the flooding process and help define the regions in the image.

The preprocessing steps are noise removal and gradient computation, which are crucial for obtaining accurate segmentation results. In a marker-based Watershed, initial markers are placed on the image to guide the flooding process. This process iteratively fills basins and resolves conflicts between regions. Post-processing steps merge and refine the segmented regions to obtain the final segmentation result.

Let’s see an example of Python code that demonstrates how to use the Watershed algorithm to label video data, using the Kinetics Human Action Video dataset.