4,753 research outputs found
Using Compressed Audio-visual Words for Multi-modal Scene Classification
We present a novel approach to scene classification using combined audio signal and video image features and compare this methodology to scene classification results using each modality in isolation. Each modality is represented using summary features, namely Mel-frequency Cepstral Coefficients (audio) and Scale Invariant Feature Transform (SIFT) (video) within a multi-resolution bag-of-features model. Uniquely, we extend the classical bag-of-words approach over both audio and video feature spaces, whereby we introduce the concept of compressive sensing as a novel methodology for multi-modal fusion via audio-visual feature dimensionality reduction. We perform evaluation over a range of environments showing performance that is both comparable to the state of the art (86%, over ten scene classes) and invariant to a ten-fold dimensionality reduction within the audio-visual feature space using our compressive representation approach
Masking Strategies for Image Manifolds
We consider the problem of selecting an optimal mask for an image manifold,
i.e., choosing a subset of the pixels of the image that preserves the
manifold's geometric structure present in the original data. Such masking
implements a form of compressive sensing through emerging imaging sensor
platforms for which the power expense grows with the number of pixels acquired.
Our goal is for the manifold learned from masked images to resemble its full
image counterpart as closely as possible. More precisely, we show that one can
indeed accurately learn an image manifold without having to consider a large
majority of the image pixels. In doing so, we consider two masking methods that
preserve the local and global geometric structure of the manifold,
respectively. In each case, the process of finding the optimal masking pattern
can be cast as a binary integer program, which is computationally expensive but
can be approximated by a fast greedy algorithm. Numerical experiments show that
the relevant manifold structure is preserved through the data-dependent masking
process, even for modest mask sizes
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of
the underlying Gaussian germ. Several rotations have been proposed in the
literature resulting in adaptations with different convergence properties. In
this paper we present a new adaptation mechanism that builds on compressive
sensing algorithms, resulting in a reduced polynomial chaos approximation with
optimal sparsity. The developed adaptation algorithm consists of a two-step
optimization procedure that computes the optimal coefficients and the input
projection matrix of a low dimensional chaos expansion with respect to an
optimally rotated basis. We demonstrate the attractive features of our
algorithm through several numerical examples including the application on
Large-Eddy Simulation (LES) calculations of turbulent combustion in a HIFiRE
scramjet engine.Comment: Submitted to Journal of Computational Physic
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