10,628 research outputs found
Unsupervised Learning of Individuals and Categories from Images
Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data
Incremental Principal Component Analysis Exact implementation and continuity corrections
This paper describes some applications of an incremental implementation of
the principal component analysis (PCA). The algorithm updates the
transformation coefficients matrix on-line for each new sample, without the
need to keep all the samples in memory. The algorithm is formally equivalent to
the usual batch version, in the sense that given a sample set the
transformation coefficients at the end of the process are the same. The
implications of applying the PCA in real time are discussed with the help of
data analysis examples. In particular we focus on the problem of the continuity
of the PCs during an on-line analysis.Comment: accepted at http://www.icinco.org
Bags of Affine Subspaces for Robust Object Tracking
We propose an adaptive tracking algorithm where the object is modelled as a
continuously updated bag of affine subspaces, with each subspace constructed
from the object's appearance over several consecutive frames. In contrast to
linear subspaces, affine subspaces explicitly model the origin of subspaces.
Furthermore, instead of using a brittle point-to-subspace distance during the
search for the object in a new frame, we propose to use a subspace-to-subspace
distance by representing candidate image areas also as affine subspaces.
Distances between subspaces are then obtained by exploiting the non-Euclidean
geometry of Grassmann manifolds. Experiments on challenging videos (containing
object occlusions, deformations, as well as variations in pose and
illumination) indicate that the proposed method achieves higher tracking
accuracy than several recent discriminative trackers.Comment: in International Conference on Digital Image Computing: Techniques
and Applications, 201
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