  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.
