Semantic segmentation and scene understanding with image color identification

Abstract

Semantic segmentation and scene understanding are pivotal in computer vision, particularly for applications like autonomous driving, medical imaging, and robotics. This paper explores how incorporating image color identification can enhance the performance and accuracy of semantic segmentation models. Through a series of experiments, we evaluate the contribution of color-based features in deep learning architectures, illustrating improvements in both object recognition and scene context awareness.