24,564 research outputs found
Image Segmentation using Sparse Subset Selection
In this paper, we present a new image segmentation method based on the
concept of sparse subset selection. Starting with an over-segmentation, we
adopt local spectral histogram features to encode the visual information of the
small segments into high-dimensional vectors, called superpixel features. Then,
the superpixel features are fed into a novel convex model which efficiently
leverages the features to group the superpixels into a proper number of
coherent regions. Our model automatically determines the optimal number of
coherent regions and superpixels assignment to shape final segments. To solve
our model, we propose a numerical algorithm based on the alternating direction
method of multipliers (ADMM), whose iterations consist of two highly
parallelizable sub-problems. We show each sub-problem enjoys closed-form
solution which makes the ADMM iterations computationally very efficient.
Extensive experiments on benchmark image segmentation datasets demonstrate that
our proposed method in combination with an over-segmentation can provide high
quality and competitive results compared to the existing state-of-the-art
methods.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Discriminative variable selection for clustering with the sparse Fisher-EM algorithm
The interest in variable selection for clustering has increased recently due
to the growing need in clustering high-dimensional data. Variable selection
allows in particular to ease both the clustering and the interpretation of the
results. Existing approaches have demonstrated the efficiency of variable
selection for clustering but turn out to be either very time consuming or not
sparse enough in high-dimensional spaces. This work proposes to perform a
selection of the discriminative variables by introducing sparsity in the
loading matrix of the Fisher-EM algorithm. This clustering method has been
recently proposed for the simultaneous visualization and clustering of
high-dimensional data. It is based on a latent mixture model which fits the
data into a low-dimensional discriminative subspace. Three different approaches
are proposed in this work to introduce sparsity in the orientation matrix of
the discriminative subspace through -type penalizations. Experimental
comparisons with existing approaches on simulated and real-world data sets
demonstrate the interest of the proposed methodology. An application to the
segmentation of hyperspectral images of the planet Mars is also presented
Spectral Unmixing with Multiple Dictionaries
Spectral unmixing aims at recovering the spectral signatures of materials,
called endmembers, mixed in a hyperspectral or multispectral image, along with
their abundances. A typical assumption is that the image contains one pure
pixel per endmember, in which case spectral unmixing reduces to identifying
these pixels. Many fully automated methods have been proposed in recent years,
but little work has been done to allow users to select areas where pure pixels
are present manually or using a segmentation algorithm. Additionally, in a
non-blind approach, several spectral libraries may be available rather than a
single one, with a fixed number (or an upper or lower bound) of endmembers to
chose from each. In this paper, we propose a multiple-dictionary constrained
low-rank matrix approximation model that address these two problems. We propose
an algorithm to compute this model, dubbed M2PALS, and its performance is
discussed on both synthetic and real hyperspectral images
- …