88,116 research outputs found
What your Facebook Profile Picture Reveals about your Personality
People spend considerable effort managing the impressions they give others.
Social psychologists have shown that people manage these impressions
differently depending upon their personality. Facebook and other social media
provide a new forum for this fundamental process; hence, understanding people's
behaviour on social media could provide interesting insights on their
personality. In this paper we investigate automatic personality recognition
from Facebook profile pictures. We analyze the effectiveness of four families
of visual features and we discuss some human interpretable patterns that
explain the personality traits of the individuals. For example, extroverts and
agreeable individuals tend to have warm colored pictures and to exhibit many
faces in their portraits, mirroring their inclination to socialize; while
neurotic ones have a prevalence of pictures of indoor places. Then, we propose
a classification approach to automatically recognize personality traits from
these visual features. Finally, we compare the performance of our
classification approach to the one obtained by human raters and we show that
computer-based classifications are significantly more accurate than averaged
human-based classifications for Extraversion and Neuroticism
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic
The AU Microscopii Debris Disk: Multiwavelength Imaging and Modeling
(abridged) Debris disks around main sequence stars are produced by the
erosion and evaporation of unseen parent bodies. AU Microscopii (GJ 803) is a
compelling object to study in the context of disk evolution across different
spectral types, as it is an M dwarf whose near edge-on disk may be directly
compared to that of its A5V sibling beta Pic. We resolve the disk from 8-60 AU
in the near-IR JHK' bands at high resolution with the Keck II telescope and
adaptive optics, and develop a novel data reduction technique for the removal
of the stellar point spread function. The point source detection sensitivity in
the disk midplane is more than a magnitude less sensitive than regions away
from the disk for some radii. We measure a blue color across the near-IR bands,
and confirm the presence of substructure in the inner disk. Some of the
structural features exhibit wavelength-dependent positions. The disk
architecture and characteristics of grain composition are inferred through
modeling. We approach the modeling of the dust distribution in a manner that
complements previous work. Using a Monte Carlo radiative transfer code, we
compare a relatively simple model of the distribution of porous grains to a
broad data set, simultaneously fitting to midplane surface brightness profiles
and the spectral energy distribution. Our model confirms that the large-scale
architecture of the disk is consistent with detailed models of steady-state
grain dynamics. Here, a belt of parent bodies from 35-40 AU is responsible for
producing dust that is then swept outward by the stellar wind and radiation
pressures. We infer the presence of very small grains in the outer region, down
to sizes of ~0.05 micron. These sizes are consistent with stellar mass-loss
rates Mdot_* << 10^2 Mdot_sun.Comment: ApJ accepted, 56 pages, preprint style. Version in emulateapj with
high-resolution figures available at http://tinyurl.com/y6ent
Accessibility-based reranking in multimedia search engines
Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context
The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits
Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)
Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents
Document Image Analysis, like any Digital Image Analysis requires
identification and extraction of proper features, which are generally extracted
from uncompressed images, though in reality images are made available in
compressed form for the reasons such as transmission and storage efficiency.
However, this implies that the compressed image should be decompressed, which
indents additional computing resources. This limitation induces the motivation
to research in extracting features directly from the compressed image. In this
research, we propose to extract essential features such as projection profile,
run-histogram and entropy for text document analysis directly from run-length
compressed text-documents. The experimentation illustrates that features are
extracted directly from the compressed image without going through the stage of
decompression, because of which the computing time is reduced. The feature
values so extracted are exactly identical to those extracted from uncompressed
images.Comment: Published by IEEE in Proceedings of ACPR-2013. arXiv admin note: text
overlap with arXiv:1403.778
- …