The Image segmentation is referred to as one of the most important processes of image processing. Image segmentation is the technique of dividing or partitioning an image into parts, called segments. It is mostly useful for applications like image compression or object recognition, because for these types of applications, it is inefficient to process the whole image. So, image segmentation is used to segment the parts from image for further processing. Semantic image segmentation is a vast area for computer vision and machine learning researchers. Many vision applications need accurate and efficient image segmentation and segment classification mechanisms for assessing the visual contents and perform the real-time decision making. There exist several image segmentation techniques, which partition the image into several parts based on certain image features like pixel intensity value, color, texture, etc. These all techniques are categorized based on the segmentation method used. The application area includes remote sensing, autonomous driving, indoor navigation, video surveillance and virtual or augmented reality systems etc. This survey paper provides a review of different traditional methods of image segmentation.

Image segmentation, Conditional Random Field, Deep learning, semantic video segmentation

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