Abstract
Semantic segmentation is a field of image content recognition in which each pixel is classified according to the type of object it belongs to, while instance segmentation distinguishes individual object instances. A novel method, BoundaryX, is proposed to unify both tasks without relying on bounding boxes. Each pixel is classified, and boundaries are drawn around separate instances, enabling easy bounding box calculation without shape constraints or region proposals. Both instanced objects (like people) and non-instanced ones (like the sky) are handled by BoundaryX, without hardcoded exceptions. The quality of the method was evaluated on the COCO dataset for the class “people” by measuring Intersection over Union (IoU) for the semantic segmentation and bounding boxes recall and precision. The method achieved 0.774 IoU for semantic segmentation, 75% recall, and 83% precision for bounding box quality. Segmentation pipelines are simplified through the unified solution and flexible boundary-based representation provided by BoundaryX.