12,397 research outputs found
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
Inferring the photometric and size evolution of galaxies from image simulations
Current constraints on models of galaxy evolution rely on morphometric
catalogs extracted from multi-band photometric surveys. However, these catalogs
are altered by selection effects that are difficult to model, that correlate in
non trivial ways, and that can lead to contradictory predictions if not taken
into account carefully. To address this issue, we have developed a new approach
combining parametric Bayesian indirect likelihood (pBIL) techniques and
empirical modeling with realistic image simulations that reproduce a large
fraction of these selection effects. This allows us to perform a direct
comparison between observed and simulated images and to infer robust
constraints on model parameters. We use a semi-empirical forward model to
generate a distribution of mock galaxies from a set of physical parameters.
These galaxies are passed through an image simulator reproducing the
instrumental characteristics of any survey and are then extracted in the same
way as the observed data. The discrepancy between the simulated and observed
data is quantified, and minimized with a custom sampling process based on
adaptive Monte Carlo Markov Chain methods. Using synthetic data matching most
of the properties of a CFHTLS Deep field, we demonstrate the robustness and
internal consistency of our approach by inferring the parameters governing the
size and luminosity functions and their evolutions for different realistic
populations of galaxies. We also compare the results of our approach with those
obtained from the classical spectral energy distribution fitting and
photometric redshift approach.Our pipeline infers efficiently the luminosity
and size distribution and evolution parameters with a very limited number of
observables (3 photometric bands). When compared to SED fitting based on the
same set of observables, our method yields results that are more accurate and
free from systematic biases.Comment: 24 pages, 12 figures, accepted for publication in A&
SInCom 2015
2nd Baden-Württemberg Center of Applied Research Symposium on Information and Communication Systems, SInCom 2015, 13. November 2015 in Konstan
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