2 research outputs found
Multiple Manifolds Metric Learning with Application to Image Set Classification
In image set classification, a considerable advance has been made by modeling
the original image sets by second order statistics or linear subspace, which
typically lie on the Riemannian manifold. Specifically, they are Symmetric
Positive Definite (SPD) manifold and Grassmann manifold respectively, and some
algorithms have been developed on them for classification tasks. Motivated by
the inability of existing methods to extract discriminatory features for data
on Riemannian manifolds, we propose a novel algorithm which combines multiple
manifolds as the features of the original image sets. In order to fuse these
manifolds, the well-studied Riemannian kernels have been utilized to map the
original Riemannian spaces into high dimensional Hilbert spaces. A metric
Learning method has been devised to embed these kernel spaces into a lower
dimensional common subspace for classification. The state-of-the-art results
achieved on three datasets corresponding to two different classification tasks,
namely face recognition and object categorization, demonstrate the
effectiveness of the proposed method.Comment: 6 pages, 4 figures,ICPR 2018(accepted
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
The importance of wild video based image set recognition is becoming
monotonically increasing. However, the contents of these collected videos are
often complicated, and how to efficiently perform set modeling and feature
extraction is a big challenge for set-based classification algorithms. In
recent years, some proposed image set classification methods have made a
considerable advance by modeling the original image set with covariance matrix,
linear subspace, or Gaussian distribution. As a matter of fact, most of them
just adopt a single geometric model to describe each given image set, which may
lose some other useful information for classification. To tackle this problem,
we propose a novel algorithm to model each image set from a multi-geometric
perspective. Specifically, the covariance matrix, linear subspace, and Gaussian
distribution are applied for set representation simultaneously. In order to
fuse these multiple heterogeneous Riemannian manifoldvalued features, the
well-equipped Riemannian kernel functions are first utilized to map them into
high dimensional Hilbert spaces. Then, a multi-kernel metric learning framework
is devised to embed the learned hybrid kernels into a lower dimensional common
subspace for classification. We conduct experiments on four widely used
datasets corresponding to four different classification tasks: video-based face
recognition, set-based object categorization, video-based emotion recognition,
and dynamic scene classification, to evaluate the classification performance of
the proposed algorithm. Extensive experimental results justify its superiority
over the state-of-the-art.Comment: 15 pages, 9 figure