1 research outputs found
Invariant Tensor Feature Coding
We propose a novel feature coding method that exploits invariance. We
consider the setting where the transformations that preserve the image contents
compose a finite group of orthogonal matrices. This is the case in many image
transformations, such as image rotations and image flipping. We prove that the
group-invariant feature vector contains sufficient discriminative information
when learning a linear classifier using convex loss minimization. From this
result, we propose a novel feature modeling for principal component analysis
and k-means clustering, which are used for most feature coding methods, and
global feature functions that explicitly consider the group action. Although
the global feature functions are complex nonlinear functions in general, we can
calculate the group action on this space easily by constructing the functions
as the tensor product representations of basic representations, resulting in
the explicit form of invariant feature functions. We demonstrate the
effectiveness of our methods on several image datasets.Comment: 14 pages, 5 figure