Department of Electrical Engineering, University of California, Riverside, CA 92521, USA.
IEEE Trans Pattern Anal Mach Intell. 2012 Sep;34(9):1827-41. doi: 10.1109/TPAMI.2011.259.
This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.
本文提出了一种新的基于对称积分的区域图像分割方法。该方法通过利用图像对称来获得改进的图像分割，其通过构建一个可以灵活嵌入到分割线索中的对称标记来实现。首先通过 SIFT 算子从图像中提取出兴趣点，并进一步对其进行细化，以检测全局双边对称。然后使用对称轴计算对称亲和矩阵，并将其明确用作区域生长算法中的约束条件，以细化分割区域的对称性。多目标遗传搜索找到具有最佳分割和对称性的分割结果，接近全局最优。该方法已在具有挑战性的自然图像和包含人造物体的图像中进行了实验研究。结果表明，所提出的方法在利用和不利用对称性的情况下都优于当前的分割方法。彻底的实验分析表明，对称性作为一种分割线索，与颜色和纹理等其他属性一起发挥着重要作用。
