Image segmentation has come a long way since the early days of computer vision, and still remains a challenging task. Modern variations of the classical (purely bottom-up) approach, involve, e.g., some form of user assistance (interactive segmentation) or ask for the simultaneous segmentation of two or more images (co-segmentation). At an abstract level, all these variants can be thought of as “constrained” versions of the original formulation, whereby the segmentation process is guided by some external source of information. In this paper, we propose a new approach to tackle this kind of problems in a unified way. Our work is based on some properties of a family of quadratic optimization problems related to dominant sets, a graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract groups of dominant-set clusters that are constrained to contain predefined elements. In particular, we shall focus on interactive segmentation and co-segmentation (in both the unsupervised and the interactive versions). The proposed algorithm can deal naturally with several types of constraints and input modalities, including scribbles, sloppy contours and bounding boxes, and is able to robustly handle noisy annotations on the part of the user. Experiments on standard benchmark datasets show the effectiveness of our approach as compared to state-of-the-art algorithms on a variety of natural images under several input conditions and constraints.

中文翻译：

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用于“约束”图像分割的主导集

自计算机视觉发展初期以来，图像分割已经走了很长一段路，并且仍然是一项艰巨的任务。经典（纯粹自下而上）方法的现代变体涉及例如某种形式的用户协助（交互式分割）或要求同时分割两个或更多图像（共同分割）。从抽象的角度来看，所有这些变体都可以被认为是原始公式的“受约束”版本，从而在分割过程中受到某些外部信息源的指导。在本文中，我们提出了一种统一解决此类问题的新方法。我们的工作基于与支配集相关的二次优化问题族的某些属性，这是一个群集的图论概念，该图论将最大集团的概念推广到边缘加权图。尤其是，我们表明，通过适当地控制确定潜在问题的结构和规模的正则化参数，我们可以提取约束为包含预定义元素的主导集聚类组。特别是，我们将专注于交互式细分和共同细分（在无监督版本和交互式版本中）。所提出的算法可以自然地处理几种类型的约束和输入模态，包括涂鸦，草率的轮廓和边界框，并且能够健壮地处理用户方面的嘈杂注释。标准基准数据集上的实验表明，与在多种输入条件和约束下对各种自然图像进行处理的最新算法相比，我们的方法是有效的。