1,869 research outputs found
Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval
In this paper we address the problem of learning robust cross-domain
representations for sketch-based image retrieval (SBIR). While most SBIR
approaches focus on extracting low- and mid-level descriptors for direct
feature matching, recent works have shown the benefit of learning coupled
feature representations to describe data from two related sources. However,
cross-domain representation learning methods are typically cast into non-convex
minimization problems that are difficult to optimize, leading to unsatisfactory
performance. Inspired by self-paced learning, a learning methodology designed
to overcome convergence issues related to local optima by exploiting the
samples in a meaningful order (i.e. easy to hard), we introduce the cross-paced
partial curriculum learning (CPPCL) framework. Compared with existing
self-paced learning methods which only consider a single modality and cannot
deal with prior knowledge, CPPCL is specifically designed to assess the
learning pace by jointly handling data from dual sources and modality-specific
prior information provided in the form of partial curricula. Additionally,
thanks to the learned dictionaries, we demonstrate that the proposed CPPCL
embeds robust coupled representations for SBIR. Our approach is extensively
evaluated on four publicly available datasets (i.e. CUFS, Flickr15K, QueenMary
SBIR and TU-Berlin Extension datasets), showing superior performance over
competing SBIR methods
Localized Manifold Harmonics for Spectral Shape Analysis
The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer graphics and geometry processing applications. In particular, Laplacian eigenbases allow generalizing the classical Fourier analysis to manifolds. A key drawback of such bases is their inherently global nature, as the Laplacian eigenfunctions carry geometric and topological structure of the entire manifold. In this paper, we introduce a new framework for local spectral shape analysis. We show how to efficiently construct localized orthogonal bases by solving an optimization problem that in turn can be posed as the eigendecomposition of a new operator obtained by a modification of the standard Laplacian. We study the theoretical and computational aspects of the proposed framework and showcase our new construction on the classical problems of shape approximation and correspondence. We obtain significant improvement compared to classical Laplacian eigenbases as well as other alternatives for constructing localized bases
WxBS: Wide Baseline Stereo Generalizations
We have presented a new problem -- the wide multiple baseline stereo (WxBS)
-- which considers matching of images that simultaneously differ in more than
one image acquisition factor such as viewpoint, illumination, sensor type or
where object appearance changes significantly, e.g. over time. A new dataset
with the ground truth for evaluation of matching algorithms has been introduced
and will be made public.
We have extensively tested a large set of popular and recent detectors and
descriptors and show than the combination of RootSIFT and HalfRootSIFT as
descriptors with MSER and Hessian-Affine detectors works best for many
different nuisance factors. We show that simple adaptive thresholding improves
Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them
on infrared and low contrast images.
A novel matching algorithm for addressing the WxBS problem has been
introduced. We have shown experimentally that the WxBS-M matcher dominantes the
state-of-the-art methods both on both the new and existing datasets.Comment: Descriptor and detector evaluation expande
Many-to-Many Graph Matching: a Continuous Relaxation Approach
Graphs provide an efficient tool for object representation in various
computer vision applications. Once graph-based representations are constructed,
an important question is how to compare graphs. This problem is often
formulated as a graph matching problem where one seeks a mapping between
vertices of two graphs which optimally aligns their structure. In the classical
formulation of graph matching, only one-to-one correspondences between vertices
are considered. However, in many applications, graphs cannot be matched
perfectly and it is more interesting to consider many-to-many correspondences
where clusters of vertices in one graph are matched to clusters of vertices in
the other graph. In this paper, we formulate the many-to-many graph matching
problem as a discrete optimization problem and propose an approximate algorithm
based on a continuous relaxation of the combinatorial problem. We compare our
method with other existing methods on several benchmark computer vision
datasets.Comment: 1
- …