Interactive Image Segmentation Using Constrained Dominant Sets
We propose a new approach to interactive image segmentation based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal cliqu
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Abstract. We propose a new approach to interactive image segmentation based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edgeweighted 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 which are constrained to contain user-selected elements. The resulting algorithm can deal naturally with any type of input modality, 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. Keywords: Interactive segmentation · Dominant sets · Quadratic optimization
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Introduction
User-assisted image segmentation has recently attracted considerable attention within the computer vision community, especially because of its potential applications in a variety of different problems such as image and video editing, medical image analysis, etc. [1–8]. Given an input image and some information provided by a user, usually in the form of a scribble or of a bounding box, the goal is to provide as output a foreground object in such a way as to best reflect the user’s intent. By exploiting high-level, semantic knowledge on the part of the user, which is typically difficult to formalize, we are therefore able to effectively solve segmentation problems which would be otherwise too complex to be tackled using fully automatic segmentation algorithms. Existing algorithms fall into two broad categories, depending on whether the user annotation is given in terms of a scribble or of a bounding box, and supporters of the two approaches have both good reasons to prefer one modality against Electronic supplementary material The online version of this chapter (doi:10. 1007/978-3-319-46484-8 17) contains supplementary material, which is available to authorized users. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VIII, LNCS 9912, pp. 278–294, 2016. DOI: 10.1007/978-3-319-46484-8 17
Interactive Image Segmentation Using Constrained Dominant Sets
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the other. For example, Wu et al. [3] claim that bounding boxes are the most natural and economical form in terms of the amount of user interaction, and develop a multiple instance learning algorithm that extracts an arbitrary object located inside a tight bounding box at unknown location. Yu et al. [9] also support the bounding-box approach, though their algorithm is different from others in that it does not need bounding boxes tightly enclosing the object of interest, whose production of course increases the annotation burden. They provide an algorithm, based on a Markov Random Field (MRF) energy function
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