Visual object clustering via mixed-norm regularization
MetadataShow full item record
Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.
Showing items related by title, author, creator and subject.
Gupta, Sunil Kumar (2011)The growing number of information sources has given rise to joint analysis. While the research community has mainly focused on analyzing data from a single source, there has been relatively few attempts on jointly analyzing ...
Techniques for improving clustering and association rules mining from very large transactional databasesLi, Yanrong (2009)Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have ...
Veen, Daniel John (2010)Smoothed Particle Hydrodynamics (SPH) is a mesh-free Lagrangian computational method suited to modelling fluids with a freely deforming surface. This thesis describes the development, validation and application of a ...