  Recently, several clustering algorithms have been used to solve variety of
problems from different discipline. This dissertation aims to address different
challenging tasks in computer vision and pattern recognition by casting the
problems as a clustering problem. We proposed novel approaches to solve
multi-target tracking, visual geo-localization and outlier detection problems
using a unified underlining clustering framework, i.e., dominant set clustering
and its extensions, and presented a superior result over several
state-of-the-art approaches.
