论文标题
基于自然图像分段的基于亲和力融合图的框架
Affinity Fusion Graph-based Framework for Natural Image Segmentation
论文作者
论文摘要
本文提出了一个亲和力融合图框架，以有效地连接不同的图形，以高度辨别的功率和非线性以进行自然图像分割。提出的框架根据一个新的定义，将邻接环和基于内核光谱聚类的图（ksc-graphs）结合在一起，该定义名为多尺度超级像素的新定义。这些亲和力节点是基于超级像素的更好隶属关系而选择的，即亚平面空间表示，该表示由基于子空间追踪的稀疏子空间聚类生成。然后，KSC图是通过新颖的内核光谱聚类构建的，以探索这些亲和力节点之间的非线性关系。此外，构建了每个量表的邻接环，该图被进一步用于更新Affition节点处提出的KSC-Graph。融合图是在不同尺度上构建的，并且可以分区以获得最终的分割结果。伯克利分割数据集和微软研究剑桥数据集的实验结果表明，与最先进的方法相比，我们的框架的优越性。该代码可在https://github.com/yangzhangcst/af-graph上找到。
This paper proposes an affinity fusion graph framework to effectively connect different graphs with highly discriminating power and nonlinearity for natural image segmentation. The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels. These affinity nodes are selected based on a better affiliation of superpixels, namely subspace-preserving representation which is generated by sparse subspace clustering based on subspace pursuit. Then a KSC-graph is built via a novel kernel spectral clustering to explore the nonlinear relationships among these affinity nodes. Moreover, an adjacency-graph at each scale is constructed, which is further used to update the proposed KSC-graph at affinity nodes. The fusion graph is built across different scales, and it is partitioned to obtain final segmentation result. Experimental results on the Berkeley segmentation dataset and Microsoft Research Cambridge dataset show the superiority of our framework in comparison with the state-of-the-art methods. The code is available at https://github.com/Yangzhangcst/AF-graph.
