87,617 research outputs found
Learning to rank in person re-identification with metric ensembles
We propose an effective structured learning based approach to the problem of
person re-identification which outperforms the current state-of-the-art on most
benchmark data sets evaluated. Our framework is built on the basis of multiple
low-level hand-crafted and high-level visual features. We then formulate two
optimization algorithms, which directly optimize evaluation measures commonly
used in person re-identification, also known as the Cumulative Matching
Characteristic (CMC) curve. Our new approach is practical to many real-world
surveillance applications as the re-identification performance can be
concentrated in the range of most practical importance. The combination of
these factors leads to a person re-identification system which outperforms most
existing algorithms. More importantly, we advance state-of-the-art results on
person re-identification by improving the rank- recognition rates from
to on the iLIDS benchmark, to on the PRID2011
benchmark, to on the VIPeR benchmark, to on the
CUHK01 benchmark and to on the CUHK03 benchmark.Comment: 10 page
Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and
Euclid necessitate automatic and efficient identification methods of strong
lensing systems. We present a strong lensing identification approach that
utilizes a feature extraction method from computer vision, the Histogram of
Oriented Gradients (HOG), to capture edge patterns of arcs. We train a
supervised classifier model on the HOG of mock strong galaxy-galaxy lens images
similar to observations from the Hubble Space Telescope (HST) and LSST. We
assess model performance with the area under the curve (AUC) of a Receiver
Operating Characteristic (ROC) curve. Models trained on 10,000 lens and
non-lens containing images images exhibit an AUC of 0.975 for an HST-like
sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST
observations. Performance appears to continually improve with the training set
size. Models trained on fewer images perform better in absence of the lens
galaxy light. However, with larger training data sets, information from the
lens galaxy actually improves model performance, indicating that HOG captures
much of the morphological complexity of the arc finding problem. We test our
classifier on data from the Sloan Lens ACS Survey and find that small scale
image features reduces the efficiency of our trained model. However, these
preliminary tests indicate that some parameterizations of HOG can compensate
for differences between observed mock data. One example best-case
parameterization results in an AUC of 0.6 in the F814 filter image with other
parameterization results equivalent to random performance.Comment: 18 pages, 14 figures, summarizing results in figure
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several
computer vision and pattern recognition problems, where graph representations
are often used to model objects or interactions between elements. The choice of
a distance or similarity metric is, however, not trivial and can be highly
dependent on the application at hand. In this work, we propose a novel metric
learning method to evaluate distance between graphs that leverages the power of
convolutional neural networks, while exploiting concepts from spectral graph
theory to allow these operations on irregular graphs. We demonstrate the
potential of our method in the field of connectomics, where neuronal pathways
or functional connections between brain regions are commonly modelled as
graphs. In this problem, the definition of an appropriate graph similarity
function is critical to unveil patterns of disruptions associated with certain
brain disorders. Experimental results on the ABIDE dataset show that our method
can learn a graph similarity metric tailored for a clinical application,
improving the performance of a simple k-nn classifier by 11.9% compared to a
traditional distance metric.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion
Most of the traditional convolutional neural networks (CNNs) implements
bottom-up approach (feed-forward) for image classifications. However, many
scientific studies demonstrate that visual perception in primates rely on both
bottom-up and top-down connections. Therefore, in this work, we propose a CNN
network with feedback structure for Solar power plant detection on
middle-resolution satellite images. To express the strength of the top-down
connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model
used for solar power plant classification on multi-spectral satellite data.
Moreover, we introduce a method to improve class activation mapping (CAM) to
our FB-Net, which takes advantage of multi-channel pulse coupled neural network
(m-PCNN) for weakly-supervised localization of the solar power plants from the
features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN,
experimental results demonstrated promising results on both solar-power plant
image classification and detection task.Comment: 9 pages, 9 figures, 4 table
Unsupervised Learning of Individuals and Categories from Images
Motivated by the existence of highly selective, sparsely firing cells observed in the human medial temporal lobe (MTL), we present an unsupervised method for learning and recognizing object categories from unlabeled images. In our model, a network of nonlinear neurons learns a sparse representation of its inputs through an unsupervised expectation-maximization process. We show that the application of this strategy to an invariant feature-based description of natural images leads to the development of units displaying sparse, invariant selectivity for particular individuals or image categories much like those observed in the MTL data
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