Weaklier-Supervised Semantic Segmentation with Pyramid Scene Parsing Network

Abstract

Semantic image segmentation is the essential task of computer vision. It requires dividing visual input into different meaningful interpretable categories. In this work image attribution and segmentation approach is proposed. It can identify complex objects present in an image. The proposed model starts with superpixelization using Simple Linear Iterative Clustering (SLIC). A Multi Heat Map Slices Fusion model (MSF) produces an object seed heat map, and a Saliency Edge Colour Texture (SECT) model generates pixel-level annotations. Lastly, the PSPNet model for developing the final semantic segmentation of the object. The proposed model was implemented, and compared with the earlier work, it excelled the performance score. © 2021 IEEE.