227 research outputs found
Simultaneous merging multiple grid maps using the robust motion averaging
Mapping in the GPS-denied environment is an important and challenging task in
the field of robotics. In the large environment, mapping can be significantly
accelerated by multiple robots exploring different parts of the environment.
Accordingly, a key problem is how to integrate these local maps built by
different robots into a single global map. In this paper, we propose an
approach for simultaneous merging of multiple grid maps by the robust motion
averaging. The main idea of this approach is to recover all global motions for
map merging from a set of relative motions. Therefore, it firstly adopts the
pair-wise map merging method to estimate relative motions for grid map pairs.
To obtain as many reliable relative motions as possible, a graph-based sampling
scheme is utilized to efficiently remove unreliable relative motions obtained
from the pair-wise map merging. Subsequently, the accurate global motions can
be recovered from the set of reliable relative motions by the motion averaging.
Experimental results carried on real robot data sets demonstrate that proposed
approach can achieve simultaneous merging of multiple grid maps with good
performances
Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval
Image retrieval based on deep convolutional features has demonstrated
state-of-the-art performance in popular benchmarks. In this paper, we present a
unified solution to address deep convolutional feature aggregation and image
re-ranking by simulating the dynamics of heat diffusion. A distinctive problem
in image retrieval is that repetitive or \emph{bursty} features tend to
dominate final image representations, resulting in representations less
distinguishable. We show that by considering each deep feature as a heat
source, our unsupervised aggregation method is able to avoid
over-representation of \emph{bursty} features. We additionally provide a
practical solution for the proposed aggregation method and further show the
efficiency of our method in experimental evaluation. Inspired by the
aforementioned deep feature aggregation method, we also propose a method to
re-rank a number of top ranked images for a given query image by considering
the query as the heat source. Finally, we extensively evaluate the proposed
approach with pre-trained and fine-tuned deep networks on common public
benchmarks and show superior performance compared to previous work.Comment: The paper has been accepted to IEEE Transactions on Multimedi
Feature Concatenation Multi-view Subspace Clustering
Multi-view clustering aims to achieve more promising clustering results than
single-view clustering by exploring the multi-view information. Since statistic
properties of different views are diverse, even incompatible, few approaches
implement multi-view clustering based on the concatenated features directly.
However, feature concatenation is a natural way to combine multiple views. To
this end, this paper proposes a novel multi-view subspace clustering approach
dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC).
Specifically, by exploring the consensus information, multi-view data are
concatenated into a joint representation firstly, then, -norm is
integrated into the objective function to deal with the sample-specific and
cluster-specific corruptions of multiple views for benefiting the clustering
performance. Furthermore, by introducing graph Laplacians of multiple views, a
graph regularized FCMSC is also introduced to explore both the consensus
information and complementary information for clustering. It is noteworthy that
the obtained coefficient matrix is not derived by directly applying the
Low-Rank Representation (LRR) to the joint view representation simply. Finally,
an effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is
designed to optimized the objective functions. Comprehensive experiments on six
real world datasets illustrate the superiority of the proposed methods over
several state-of-the-art approaches for multi-view clustering
Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors
This paper proposes a global approach for the multi-view registration of
unordered range scans. As the basis of multi-view registration, pair-wise
registration is very pivotal. Therefore, we first select a good descriptor and
accelerate its correspondence propagation for the pair-wise registration. Then,
we design an effective rule to judge the reliability of pair-wise registration
results. Subsequently, we propose a model augmentation method, which can
utilize reliable results of pair-wise registration to augment the model shape.
Finally, multi-view registration can be accomplished by operating the pair-wise
registration and judgment, and model augmentation alternately. Experimental
results on public available data sets show, that this approach can
automatically achieve the multi-view registration of unordered range scans with
good accuracy and effectiveness
Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions
Recently, the low rank and sparse (LRS) matrix decomposition has been
introduced as an effective mean to solve the multi-view registration. It views
each available relative motion as a block element to reconstruct one matrix so
as to approximate the low rank matrix, where global motions can be recovered
for multi-view registration. However, this approach is sensitive to the
sparsity of the reconstructed matrix and it treats all block elements equally
in spite of their varied reliability. Therefore, this paper proposes an
effective approach for multi-view registration by the weighted LRS
decomposition. Based on the anti-symmetry property of relative motions, it
firstly proposes a completion strategy to reduce the sparsity of the
reconstructed matrix. The reduced sparsity of reconstructed matrix can improve
the robustness of LRS decomposition. Then, it proposes the weighted LRS
decomposition, where each block element is assigned with one estimated weight
to denote its reliability. By introducing the weight, more accurate
registration results can be recovered from the estimated low rank matrix with
good efficiency. Experimental results tested on public data sets illustrate the
superiority of the proposed approach over the state-of-the-art approaches on
robustness, accuracy, and efficiency
Adaptive Co-weighting Deep Convolutional Features For Object Retrieval
Aggregating deep convolutional features into a global image vector has
attracted sustained attention in image retrieval. In this paper, we propose an
efficient unsupervised aggregation method that uses an adaptive Gaussian filter
and an elementvalue sensitive vector to co-weight deep features. Specifically,
the Gaussian filter assigns large weights to features of region-of-interests
(RoI) by adaptively determining the RoI's center, while the element-value
sensitive channel vector suppresses burstiness phenomenon by assigning small
weights to feature maps with large sum values of all locations. Experimental
results on benchmark datasets validate the proposed two weighting schemes both
effectively improve the discrimination power of image vectors. Furthermore,
with the same experimental setting, our method outperforms other very recent
aggregation approaches by a considerable margin.Comment: 6 pages,5 figures,ICME2018 poste
Generalized Label Enhancement with Sample Correlations
Recently, label distribution learning (LDL) has drawn much attention in
machine learning, where LDL model is learned from labelel instances. Different
from single-label and multi-label annotations, label distributions describe the
instance by multiple labels with different intensities and accommodate to more
general scenes. Since most existing machine learning datasets merely provide
logical labels, label distributions are unavailable in many real-world
applications. To handle this problem, we propose two novel label enhancement
methods, i.e., Label Enhancement with Sample Correlations (LESC) and
generalized Label Enhancement with Sample Correlations (gLESC). More
specifically, LESC employs a low-rank representation of samples in the feature
space, and gLESC leverages a tensor multi-rank minimization to further
investigate the sample correlations in both the feature space and label space.
Benefitting from the sample correlations, the proposed methods can boost the
performance of label enhancement. Extensive experiments on 14 benchmark
datasets demonstrate the effectiveness and superiority of our methods
Effective scaling registration approach by imposing the emphasis on the scale factor
This paper proposes an effective approach for the scaling registration of
-D point sets. Different from the rigid transformation, the scaling
registration can not be formulated into the common least square function due to
the ill-posed problem caused by the scale factor. Therefore, this paper designs
a novel objective function for the scaling registration problem. The appearance
of this objective function is a rational fraction, where the numerator item is
the least square error and the denominator item is the square of the scale
factor. By imposing the emphasis on scale factor, the ill-posed problem can be
avoided in the scaling registration. Subsequently, the new objective function
can be solved by the proposed scaling iterative closest point (ICP) algorithm,
which can obtain the optimal scaling transformation. For the practical
applications, the scaling ICP algorithm is further extended to align partially
overlapping point sets. Finally, the proposed approach is tested on public data
sets and applied to merging grid maps of different resolutions. Experimental
results demonstrate its superiority over previous approaches on efficiency and
robustness.Comment: 22 pages, 4 figures, 2 table
Domain segmentation and adjustment for generalized zero-shot learning
In the generalized zero-shot learning, synthesizing unseen data with
generative models has been the most popular method to address the imbalance of
training data between seen and unseen classes. However, this method requires
that the unseen semantic information is available during the training stage,
and training generative models is not trivial. Given that the generator of
these models can only be trained with seen classes, we argue that synthesizing
unseen data may not be an ideal approach for addressing the domain shift caused
by the imbalance of the training data. In this paper, we propose to realize the
generalized zero-shot recognition in different domains. Thus, unseen (seen)
classes can avoid the effect of the seen (unseen) classes. In practice, we
propose a threshold and probabilistic distribution joint method to segment the
testing instances into seen, unseen and uncertain domains. Moreover, the
uncertain domain is further adjusted to alleviate the domain shift. Extensive
experiments on five benchmark datasets show that the proposed method exhibits
competitive performance compared with that based on generative models
An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments
For the registration of partially overlapping point clouds, this paper
proposes an effective approach based on both the hard and soft assignments.
Given two initially posed clouds, it firstly establishes the forward
correspondence for each point in the data shape and calculates the value of
binary variable, which can indicate whether this point correspondence is
located in the overlapping areas or not. Then, it establishes the bilateral
correspondence and computes bidirectional distances for each point in the
overlapping areas. Based on the ratio of bidirectional distances, the
exponential function is selected and utilized to calculate the probability
value, which can indicate the reliability of the point correspondence.
Subsequently, both the values of hard and soft assignments are embedded into
the proposed objective function for registration of partially overlapping point
clouds and a novel variant of ICP algorithm is proposed to obtain the optimal
rigid transformation. The proposed approach can achieve good registration of
point clouds, even when their overlap percentage is low. Experimental results
tested on public data sets illustrate its superiority over previous approaches
on accuracy and robustness.Comment: 23 pages, 6 figures, 2 table
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