15,913 research outputs found
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2014
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Sketch-based subspace clustering of hyperspectral images
Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images
Unified Spectral Clustering with Optimal Graph
Spectral clustering has found extensive use in many areas. Most traditional
spectral clustering algorithms work in three separate steps: similarity graph
construction; continuous labels learning; discretizing the learned labels by
k-means clustering. Such common practice has two potential flaws, which may
lead to severe information loss and performance degradation. First, predefined
similarity graph might not be optimal for subsequent clustering. It is
well-accepted that similarity graph highly affects the clustering results. To
this end, we propose to automatically learn similarity information from data
and simultaneously consider the constraint that the similarity matrix has exact
c connected components if there are c clusters. Second, the discrete solution
may deviate from the spectral solution since k-means method is well-known as
sensitive to the initialization of cluster centers. In this work, we transform
the candidate solution into a new one that better approximates the discrete
one. Finally, those three subtasks are integrated into a unified framework,
with each subtask iteratively boosted by using the results of the others
towards an overall optimal solution. It is known that the performance of a
kernel method is largely determined by the choice of kernels. To tackle this
practical problem of how to select the most suitable kernel for a particular
data set, we further extend our model to incorporate multiple kernel learning
ability. Extensive experiments demonstrate the superiority of our proposed
method as compared to existing clustering approaches.Comment: Accepted by AAAI 201
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.Comment: 15 pages, 10 figures and 5 table
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