Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition
This work addresses challenging tasks in computer vision and pattern recognition, but it appears incremental as it applies an existing clustering framework to new problems.
The dissertation tackled multi-target tracking, visual geo-localization, and outlier detection by framing them as clustering problems using a unified framework based on dominant set clustering, achieving superior results over state-of-the-art approaches.
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.