基于SLIC的自适应多主体图像分割算法

为了解决多主体图像分割的交互分割问题，提出了一种基于SLIC超像素的自适应图像分割算法。首先利用SLIC对图像进行超像素分割处理，把原图像分割为大小相似、形状规则的超像素，以超像素中心点的五维特征值作为原始数据点通过自适应参数的DBSCAN算法聚类，确定多主体数目和分割边界。算法不需要用户交互，自适应确定分割数目。为了验证算法的有效性，在伯克利大学标准数据集BSDS500上与人工标注的分割图像进行比较， 前期的超像素处理使算法在时间上有很好的提升，对于一幅481×321像素的图像，只需要1.5 s就可以获得结果。实验结果表明，该方法可以有效解决多主体图像分割中的人工交互问题，同时在PRI和VOI的指数对比上也优于传统算法，本文算法可以在保证分割效果的基础上自适应确定分割数目，提高分割效率。

An adaptive multiphase image segmentation algorithm based on SLIC

Abstract:We propose an adaptive multiphase image segmentation algorithm based on SLIC to solve the problem of the interactive segmentation of multiphase images. Firstly，the new algorithm uses the SLIC method to segment image into super pixels with similar size and regular shape. As the original data points, the five dimensional eigenvalues of the super pixel center point are clustered by the DBSCAN algorithm with adaptive parameters. Meanwhile, segmentation boundary lines and the number of multiphase can also be obtained. The algorithm can determine the number of segmentations adaptively without user interaction requirement. In order to verify the effectiveness of the proposed algorithm, we make a comparison with the artificial annotation of the segmented image on the standard data set of Berkeley College (BSDS500). Because of the SLIC algorithm, the proposed algorithm can segment an image with 481×321 pixels in only 1.5 seconds. Experimental results show that it can effectively solve the problem of manual interaction in multiphase image segmentation while the PRI and VOI indexes are superior to traditional algorithms. Our algorithm can adaptively determine the number of segmentations and improve the efficiency of segmentation with good segmentation effect.