Image Object Extraction Based on Semantic Segmentation and Label Loss

Abstract

Object extraction refers to the operation of obtaining an object area from an image based on a small amount of mark information given by users, which is a key step in image processing. In order to obtain a complete object profile, current methods usually require a large number of manual annotations, especially for objects with irregular contours. Since traditional algorithms rely on low-level pixel features without semantic information, and are based on obvious mathematical assumptions (ie, strong inductive bias), it is difficult to completely identify objects. At present, in order to improve the integrity of object extraction, semantic segmentation-based methods increase the complexity and latancy by adding more pre-processing and post-processing steps. In this paper, we propose a novel model named IOEBSS, which includes a fast binary plane pre-processing, an improved Deeplab v3+ semantic segmentation model, and an auxiliary loss function named Label Loss. Through the fast binary plane pre-processing, the model can accelerate the transformation of interactive inputs. The improved semantic segmentation model makes the extracted results more semantically complete, and Label Loss is more conducive to gradient flow and accelerates training convergence. For the above reasons, IOEBSS can accurately and quickly identify objects with complex contours and colors. On Pascal VOC and COCO datasets, compared to current methods, IOEBSS has a significant improvement in accuracy, inference speed, and convergence speed.