论文标题
受限的主导集及其在计算机视觉中的应用
Constrained Dominant sets and Its applications in computer vision
论文作者
论文摘要
在本论文中，我们提出了新方案，该方案利用了一种有限的聚类方法来解决从图像检索，图像分割和共段到人重新识别的几个计算机视觉任务。在过去的几十年中，聚类方法在计算机视觉应用中发挥了至关重要的作用。在此，我们专注于众所周知的图形和游戏理论聚类方法的扩展，重新制定和集成，称为主要集合。因此，我们通过在几个基准数据集上进行的广泛实验证明了所提出的方法的有效性。
In this thesis, we present new schemes which leverage a constrained clustering method to solve several computer vision tasks ranging from image retrieval, image segmentation and co-segmentation, to person re-identification. In the last decades clustering methods have played a vital role in computer vision applications; herein, we focus on the extension, reformulation, and integration of a well-known graph and game theoretic clustering method known as Dominant Sets. Thus, we have demonstrated the validity of the proposed methods with extensive experiments which are conducted on several benchmark datasets.
