17 papers with code • 0 benchmarks • 1 datasets
Face Clustering in the videos
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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.
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.
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.
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.
With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.
Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images.
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
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).
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.