Though performed almost effortlessly by humans, segmenting 2D gray-scale or color images in terms of their constituent regions of interest (e.g.~background, objects or portions of objects) constitutes one of the greatest challenges in science and technology as a consequence of the involved dimensionality reduction(3D to 2D), noise, reflections, shades, and occlusions, among many other possible effects. While a large number of interesting approaches have been respectively suggested along the last decades, it was mainly with the more recent development of deep learning that more effective and general solutions have been obtained, currently constituting the basic comparison reference for this type of operation. Also developed recently, a multiset-based methodology has been described that is capable of encouraging performance that combines spatial accuracy, stability, and robustness while requiring minimal computational resources (hardware and/or training and recognition time). The interesting features of the latter methodology mostly follow from the enhanced selectivity and sensitivity, as well as good robustness to data perturbations and outliers, allowed by the coincidence similarity index on which the multiset approach to supervised image segmentation is based. After describing the deep learning and multiset approaches, the present work develops two comparison experiments between them which are primarily aimed at illustrating their respective main interesting features when applied to the adopted specific type of data and parameter configurations. While the deep learning approach confirmed its potential for performing image segmentation, the alternative multiset methodology allowed for encouraging accuracy while requiring little computational resources.

翻译：尽管人类几乎毫不费力地完成图像分割，但将二维灰度或彩色图像按其感兴趣的组成区域（例如背景、物体或物体部分）进行分割，由于涉及维度降低（从三维到二维）、噪声、反射、阴影和遮挡等多种可能效应，仍然是科学技术领域中的最大挑战之一。过去几十年间，学者们提出了大量有趣的方法，但直到深度学习技术的近期发展，才获得了更有效且通用的解决方案，目前该方法已成为此类任务的基本比较基准。近年来还发展出一种基于多重集的方法论，该方法能够在仅需最少计算资源（硬件及/或训练与识别时间）的同时，展现出联合空间精度、稳定性和鲁棒性的优异性能。后者方法论的有趣特性主要源于其所基于的相似度重合指标——该指标是多重集监督式图像分割方法的基础——提供的增强选择性和敏感性，以及对数据扰动和异常值的良好鲁棒性。在分别阐述深度学习方法和多重集方法后，本研究设计了两项对比实验，主要目的在于展示这两种方法在应用于特定数据类型和参数配置时各自的主要有趣特性。深度学习验证了其在图像分割中的潜力，而替代性的多重集方法则在需要极少计算资源的同时实现了令人鼓舞的精度。