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In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

Recently, sparse representation based classification has been widely used in pattern recognition. Most of existing methods exploit the recovered representation coefficients to reconstruct the inputs, and the classwise reconstruction errors are used to identify the class of the sample based on the subspace assumption. Different from the reconstruction pipeline, an assignment framework is built on the representation coefficients in this paper.

We present a geometry-inspired characterization of
target response for active sonar that exploits similarity between
intra-class features to distinguish between different targets
against environmental objects such as a rock. Key innovation is to
represent feature manifolds as a set of ellipsoids, each of which
geometrically encompasses a unique physical characteristic of
the target’s response. We have demonstrated over experimental
field data that for a given target class, these feature ellipsoids

Presentation slides covering:

- robust foreground detection / background subtraction via patch-based analysis
- person re-identification based on representations on Riemannian manifolds
- robust object tracking via Grassmann manifolds
- adapting the lessons from big data to computer vision
- future paradigm shifts: computer vision based on networks of neurosynaptic cores