Face Clustering in the videos
With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.
The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.
In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
Ranked #4 on Motion Segmentation on Hopkins155
Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing.
Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success.
Ranked #6 on Image Clustering on Extended Yale-B
In this paper, we address video face clustering using unsupervised methods.
We address the problem of face clustering in long, real world videos. This is a challenging task because faces in such videos exhibit wid evariability in scale, pose, illumination, expressions, and may also be partially occluded.
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i. e., separating points drawn from a union of subspaces).
Ranked #2 on Motion Segmentation on Hopkins155
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.