In this video, we're going to learn what is semantic image segmentation and which approaches are there to tackle this complicated problem in computer vision. Image segmentation task means splitting an image into groups of pixels by a certain criterion. As a result, we get compact representation for image data in terms of a set of components that share common visual properties. Here's an example on the slide. Now let's look at the various image segmentation problems. In semantic segmentation, each pixel of the image corresponds to a certain class. This task is complicated because different pixels of the same object differ significantly from one another in terms of features; brightness, color, or texture. The only thing they have in common is semantics, because they belong to the same identity. As you can see in the example, buildings and cars have different colors and shapes. Context has the significant importance in the semantic segmentation problem. Instance segmentation is the task of labeling all individual instances of objects of a given class. Each object is marked with his own identifier. For example, select all cars in the picture and assign a unique label to each car. The problem can be considered as dividing the image into a background and object, where each object that is not a background is marked with his own label. Object extraction involves selecting an object that is interactively defined by the user or in some other way. In co-segmentation task, we should select the same instance of an object on all images in the collection. Unsupervised segmentation involves grouping image pixels into regions whose statistical characteristics, like color or texture, are homogeneous or stationary and differ from neighboring regions. You can see an image that segments, includes pixels with the same color and don't have clear boundaries separating one segment from another. In addition, such segments usually are limited and the methods usually contain segments size limits. So even if some segments are very similar in characteristics, they will be separated. What conditions should the segments satisfy? First, the segment boundaries must correspond to the boundaries of objects. The segments must be contained entirely within the owner object. Small objects should not be a part of a segment, but should be described by their own segment. The segments should be uniform in terms of visual characteristics. They should be large enough to be informative. And they should be compact, meaning, they have about the same size. Furthermore, they are usually evenly distributed over the image and the segmentation algorithm is expected to work in some quick time. To summarize, image segmentation means assigning a class to every pixel in the image. Semantic segmentation is something that labels instances of the same class with the same label, while instance segmentation distinguishes between instances. There is unsupervised segmentation that groups pixels into regions with similar statistical characteristics.