中文摘要：

针对谱聚类方法在图像分割时的高复杂性，提出了一种基于归一化割（Ncut）的改进的谱聚类文本图像分割方法。该方法以经过量化后的颜色集合作为图分割中的顶点以简化加权图模型，从而显著降低谱聚类时的计算复杂性。首先根据文本图像特点建立相似性权值函数，然后根据场景文本颜色分布特性按照颜色直方图对色彩空间进行量化。并以量化后的颜色等级为单位构造相似矩阵，最后在Ncut准则下利用谱聚类方法实现图像分割。在包括ICDAR2009、2003竞赛测试集以及其他大量文本图像上的实验表明，该方法具有良好的文本分割性能。

英文摘要：

This paper proposes an improved spectral clustering method for image segmentation based on normalized cut （Ncut）. In order to effectively reduce the computational complexity of spectral clustering, the method uses color sets quantized as vertexes of graphs to simplify the weighted graph model. Firstly, the similarity function is estab- lished according to the characteristics of text images. And then, the color space is quantified by using the color his- togram according to the color distribution of scene images, and the affinity matrix is constructed under the quantized levels. Finally the method uses the spectral clustering to segment images under the Ncut criterion. The experiments conducted with a large number of scene images including a publicly available database from the contest of ICDAR 2009 and 2003 show that the proposed method has the good oerformanc~ in t~xt imn~o ~o＇mpnlntinn