LEARNING-BASED APPROACH FOR AUTOMATIC SEGMENTATION OF IMAGES

One of the key fields of study for computer vision applications is image segmentation. Often, segmentation is used as a preprocessing step in various real-world applications. The existing literature has revealed that image segmentation can be done with heuristic and learning-based techniques. With the emergence of artificial intelligence, learning-based approaches became popular in computer vision applications. However, there is a need to explore ML and DL-based methods with optimizations geared towards image segmentation efficiency. In this document, we suggested an algorithm known as Spectral Clustering-Based Image Segmentation (SCB-IS), which exploits the spectral clustering process for efficient image segmentation. We also proposed an approach to segmentation based on deep learning that exploits SegNet, which is widely used for image segmentation. We proposed another algorithm known as Deep Learning-Based Image Segmentation (DLB-IS). Our empirical study with benchmark datasets revealed that the proposed deep learning-based algorithm outperforms many cutting-edge deep learning models, featuring the highest accuracy of 88.50%. The suggested learning-based approaches can be integrated into real-world computer vision applications for efficient segmentation of images.