442 research outputs found
Seeing Behind the Camera: Identifying the Authorship of a Photograph
We introduce the novel problem of identifying the photographer behind a
photograph. To explore the feasibility of current computer vision techniques to
address this problem, we created a new dataset of over 180,000 images taken by
41 well-known photographers. Using this dataset, we examined the effectiveness
of a variety of features (low and high-level, including CNN features) at
identifying the photographer. We also trained a new deep convolutional neural
network for this task. Our results show that high-level features greatly
outperform low-level features. We provide qualitative results using these
learned models that give insight into our method's ability to distinguish
between photographers, and allow us to draw interesting conclusions about what
specific photographers shoot. We also demonstrate two applications of our
method.Comment: Dataset downloadable at http://www.cs.pitt.edu/~chris/photographer To
Appear in CVPR 201
Testing quantum mechanics: a statistical approach
As experiments continue to push the quantum-classical boundary using
increasingly complex dynamical systems, the interpretation of experimental data
becomes more and more challenging: when the observations are noisy, indirect,
and limited, how can we be sure that we are observing quantum behavior? This
tutorial highlights some of the difficulties in such experimental tests of
quantum mechanics, using optomechanics as the central example, and discusses
how the issues can be resolved using techniques from statistics and insights
from quantum information theory.Comment: v1: 2 pages; v2: invited tutorial for Quantum Measurements and
Quantum Metrology, substantial expansion of v1, 19 pages; v3: accepted; v4:
corrected some errors, publishe
Some Issues in the Art Image Database Systems
In this paper we illustrate several aspects of art databases, such as: the spread of the multimedia art images; the main characteristics of art images; main art images search models; unique characteristics for art image retrieval; the importance of the sensory and semantic gaps. In addition, we present several interesting features of an art image database, such as: image indexing; feature extraction; analysis on various levels of precision; style classification. We stress color features and their base, painting analysis and painting styles. We study also which MPEG-7 descriptors are best for fine painting images retrieval. An experimental system is developed to see how these descriptors work on 900 art images from several remarkable art periods. On the base of our experiments some suggestions for improving the process of searching and analysis of fine art images are given
What's unusual in online disease outbreak news?
Background: Accurate and timely detection of public health events of
international concern is necessary to help support risk assessment and response
and save lives. Novel event-based methods that use the World Wide Web as a
signal source offer potential to extend health surveillance into areas where
traditional indicator networks are lacking. In this paper we address the issue
of systematically evaluating online health news to support automatic alerting
using daily disease-country counts text mined from real world data using
BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration
detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance
against expert moderated ProMED-mail postings. Results: We report sensitivity,
specificity, positive predictive value (PPV), negative predictive value (NPV),
mean alerts/100 days and F1, at 95% confidence interval (CI) for 287
ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period.
Results indicate that W2 had the best F1 with a slight benefit for day of week
effect over C2. In drill down analysis we indicate issues arising from the
granular choice of country-level modeling, sudden drops in reporting due to day
of week effects and reporting bias. Automatic alerting has been implemented in
BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news
alerts have the potential to enhance manual analytical methods by increasing
throughput, timeliness and detection rates. Systematic evaluation of health
news aberrations is necessary to push forward our understanding of the complex
relationship between news report volumes and case numbers and to select the
best performing features and algorithms
A Data Set and a Convolutional Model for Iconography Classification in Paintings
Iconography in art is the discipline that studies the visual content of
artworks to determine their motifs and themes andto characterize the way these
are represented. It is a subject of active research for a variety of purposes,
including the interpretation of meaning, the investigation of the origin and
diffusion in time and space of representations, and the study of influences
across artists and art works. With the proliferation of digital archives of art
images, the possibility arises of applying Computer Vision techniques to the
analysis of art images at an unprecedented scale, which may support iconography
research and education. In this paper we introduce a novel paintings data set
for iconography classification and present the quantitativeand qualitative
results of applying a Convolutional Neural Network (CNN) classifier to the
recognition of the iconography of artworks. The proposed classifier achieves
good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73%
Average Precision) in the task of identifying saints in Christian religious
paintings, a task made difficult by the presence of classes with very similar
visual features. Qualitative analysis of the results shows that the CNN focuses
on the traditional iconic motifs that characterize the representation of each
saint and exploits such hints to attain correct identification. The ultimate
goal of our work is to enable the automatic extraction, decomposition, and
comparison of iconography elements to support iconographic studies and
automatic art work annotation.Comment: Published at ACM Journal on Computing and Cultural Heritage (JOCCH)
https://doi.org/10.1145/345888
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