Constrained Dominant sets and Its applications in computer vision
This work addresses computer vision problems like image retrieval and segmentation, but it is incremental as it builds on an existing clustering method.
The authors tackled multiple computer vision tasks, including image retrieval, segmentation, and person re-identification, by extending and integrating the Dominant Sets clustering method, demonstrating validity through extensive experiments on benchmark datasets.
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.