Efficient Automatic Segmentation of Multi-Domain Imagery Using Ensemble Feature-Segmenter Pairs with Machine Learning

Abstract

Automatic image segmentation refers to a field of study wherein images are analysed using complex colour, texture and shape-based features to decide the best possible segmentation configuration. These configurations differ in terms of algorithmic constants, image size, enhancement factors, edge thresholds, etc. To determine these constants, automatic segmentation algorithms use bio-inspired techniques like Genetic Algorithm (GA), particle swarm optimization (PSO), etc. These algorithms require re-training and re-evaluation whenever the input image type is changed. For instance, different set of edge thresholds are needed for medical resonance imagery (MRI) & natural images. Due to which a single algorithm is not applicable to solve the problem of multi-domain automatic image segmentation. To remove this drawback, this text proposes a novel ensemble-learning-based algorithm which uses feature-segmenter pairs for effective segmentation. The proposed approach is compared with existing state-of-the-art algorithms, and is found to have better peak-signal-to-noise ratio (PSNR) and moderate delay. The PSNR is improved by 10%, while keeping an optimum probabilistic random index (PRI) and delay performance.