Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Song Wang, Toshiro Kubota, Jeffrey Siskind
This paper presents a novel graph-theoretic approach, named ratio con- tour, to extract perceptually salient boundaries from a set of noisy bound- ary fragments detected in real images. The boundary saliency is deﬁned using the Gestalt laws of closure, proximity, and continuity. This pa- per ﬁrst constructs an undirected graph with two different sets of edges: solid edges and dashed edges. The weights of solid and dashed edges measure the local saliency in and between boundary fragments, respec- tively. Then the most salient boundary is detected by searching for an optimal cycle in this graph with minimum average weight. The proposed approach guarantees the global optimality without introducing any biases related to region area or boundary length. We collect a variety of images for testing the proposed approach with encouraging results.