[PDF][PDF] Examples of set-to-set image classification
Seventh International Conference on Mathematics in Signal Processing …, 2006•Citeseer
We present a framework for representing a set of images as a point on a Grassmann
manifold. A collection of sets of images for a specific class is then associated with a
collection of points on this manifold. Relationships between classes as defined by points
associated with sets of images may be determined using the projection F-norm, geodesic (or
other) distances on the Grassmann manifold. We present several applications of this
approach for image classification.
manifold. A collection of sets of images for a specific class is then associated with a
collection of points on this manifold. Relationships between classes as defined by points
associated with sets of images may be determined using the projection F-norm, geodesic (or
other) distances on the Grassmann manifold. We present several applications of this
approach for image classification.
Abstract
We present a framework for representing a set of images as a point on a Grassmann manifold. A collection of sets of images for a specific class is then associated with a collection of points on this manifold. Relationships between classes as defined by points associated with sets of images may be determined using the projection F-norm, geodesic (or other) distances on the Grassmann manifold. We present several applications of this approach for image classification.
Citeseer
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